text:
How can Iso-Seq reverse transcriptase artifacts be avoided?
My end goal is annotate a de-novo assembled genome. When trying to select the best method for transcriptome assembly I read Iso-seq was the preferred method. However other people suggested Nanopore as reverse transcriptase artifacts can be avoided. Is there any other way one can avoid these artifacts without the need for Nanopore?
Also, what is the recommended depth? Is using more SMRTcells worth the cost?

It is possible to filter out iso-seq reads with RT artifacts, though I don't know how effective these informatics approaches are. With nanopore, you will get more false junctions in alignment or transcript assembly due to sequencing errors. I more often see iso-seq used for new species.

Do you think polishing with RNA-seq reads is worth it? Or is Iso-Seq alone good enough?

@Caterina My other answer is also applied here: polishing iso-seq data with short reads is likely to reduce the accuracy.


text:
Can multiplexing in Sequel II SMRTcells reduce the coverage?
I would like to have high depth of coverage of the transcriptome, but I need to analyze several tissues. I was suggested to pool all 5 tissue samples in same SMRT 8M cell. Will this result in a smaller coverage when compared to non-multiplex version?

Well, necessarily multiplexing reduces coverage of each sample. 1M reads / 1 sample > 1M reads / 5 samples (assuming even coverage). Is this what you are asking?

Yes, all right makes sense. Just another question. Are these reads per base pair or per insert?

A little unsure what that's asking, but if I understand you to mean library inserts, it will be reads per insert (species of DNA in the library). That should be strongly correlated to genome/transcriptome bases covered, so unless you have some rather wacky insert sizes it will imply reads per base as well.

source: Stack Exchange

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

sort: votes


text:
How can I measure the distance between each pair of carbon alphas in overlayed protein structures?
I am trying to make a figure in which the protein structures are colored by the distance between the calphas in the overlayed structures.
I am comparing our cryoEM structure to that of a previous crystal structure and trying to show the major distance shifts in an easy to look at manner. I can get the RMSD using some online servers but it doesn't give me a good visual way of looking at the data.

Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.

Not an expert here, but you could try to create a pdb with only c-alpha where B factor column has value according to bond lenght (proportional to it) and then color by b factor spectrum, I am sure there is some command about this : https://pymolwiki.org/index.php/Spectrum

Ok, quite late.... my code stolen from https://pymolwiki.org/index.php/Spectrum as per in my comment:

Not an expert here, but you could try to create a pdb with only c-alpha where B factor column has value according to bond lenght (proportional to it) and then color by b factor spectrum, I am sure there is some command about this : pymolwiki.org/index.php/Spectrum –

code using a fake peptide created with cmd.fab('AFGVAL', 'peptide_1') :
peptide_1.pdb :
ATOM      1  N   ALA     1      19.613   5.728  -6.281  1.00  0.00           N
ATOM      2  CA  ALA     1      20.289   7.022  -6.281  1.00  0.00           C
ATOM      3  C   ALA     1      21.789   6.848  -6.281  1.00  0.00           C
ATOM      4  O   ALA     1      22.320   5.731  -6.292  1.00  0.00           O
ATOM      5  CB  ALA     1      19.781   7.814  -5.064  1.00  0.00           C
ATOM      6  H   ALA     1      20.159   4.796  -6.281  1.00  0.00           H
ATOM      7  HA  ALA     1      20.021   7.561  -7.209  1.00  0.00           H
ATOM      8 1HB  ALA     1      18.685   7.964  -5.100  1.00  0.00           H
ATOM      9 2HB  ALA     1      20.005   7.300  -4.109  1.00  0.00           H
ATOM     10 3HB  ALA     1      20.237   8.819  -5.007  1.00  0.00           H
ATOM     11  N   PHE     2      22.540   7.897  -6.270  1.00  0.00           N
ATOM     12  CA  PHE     2      23.990   7.730  -6.270  1.00  0.00           C
ATOM     13  C   PHE     2      24.690   9.068  -6.255  1.00  0.00           C
ATOM     14  O   PHE     2      24.050  10.156  -6.244  1.00  0.00           O
ATOM     15  CB  PHE     2      24.437   6.942  -7.538  1.00  0.00           C
ATOM     16  CG  PHE     2      23.824   5.546  -7.729  1.00  0.00           C
ATOM     17  CD1 PHE     2      22.610   5.411  -8.413  1.00  0.00           C
ATOM     18  CD2 PHE     2      24.430   4.416  -7.174  1.00  0.00           C
ATOM     19  CE1 PHE     2      22.002   4.165  -8.523  1.00  0.00           C
ATOM     20  CE2 PHE     2      23.826   3.167  -7.294  1.00  0.00           C
ATOM     21  CZ  PHE     2      22.611   3.043  -7.966  1.00  0.00           C
ATOM     22  H   PHE     2      22.130   8.821  -6.260  1.00  0.00           H
ATOM     23  HA  PHE     2      24.287   7.177  -5.358  1.00  0.00           H
ATOM     24 2HB  PHE     2      25.541   6.842  -7.539  1.00  0.00           H
ATOM     25 3HB  PHE     2      24.233   7.549  -8.444  1.00  0.00           H
ATOM     26 1HD  PHE     2      22.116   6.283  -8.820  1.00  0.00           H
ATOM     27 2HD  PHE     2      25.362   4.509  -6.633  1.00  0.00           H
ATOM     28 1HE  PHE     2      21.050   4.077  -9.026  1.00  0.00           H
ATOM     29 2HE  PHE     2      24.295   2.298  -6.857  1.00  0.00           H
ATOM     30  HZ  PHE     2      22.134   2.079  -8.049  1.00  0.00           H
ATOM     31  N   GLY     3      25.980   9.099  -6.253  1.00  0.00           N
ATOM     32  CA  GLY     3      26.656  10.393  -6.239  1.00  0.00           C
ATOM     33  C   GLY     3      28.155  10.220  -6.239  1.00  0.00           C
ATOM     34  O   GLY     3      28.685   9.105  -6.251  1.00  0.00           O
ATOM     35  H   GLY     3      26.513   8.242  -6.261  1.00  0.00           H
ATOM     36 3HA  GLY     3      26.365  10.908  -5.371  0.00  0.00           H
ATOM     37  HA  GLY     3      26.378  10.953  -7.151  1.00  0.00           H
ATOM     38  N   VAL     4      28.906  11.269  -6.227  1.00  0.00           N
ATOM     39  CA  VAL     4      30.356  11.101  -6.228  1.00  0.00           C
ATOM     40  C   VAL     4      31.055  12.440  -6.212  1.00  0.00           C
ATOM     41  O   VAL     4      30.429  13.507  -6.201  1.00  0.00           O
ATOM     42  CB  VAL     4      30.798  10.251  -7.484  1.00  0.00           C
ATOM     43  CG1 VAL     4      32.326  10.136  -7.737  1.00  0.00           C
ATOM     44  CG2 VAL     4      30.276   8.794  -7.470  1.00  0.00           C
ATOM     45  H   VAL     4      28.497  12.192  -6.217  1.00  0.00           H
ATOM     46  HA  VAL     4      30.654  10.571  -5.305  1.00  0.00           H
ATOM     47  HB  VAL     4      30.366  10.744  -8.381  1.00  0.00           H
ATOM     48 1HG1 VAL     4      32.862   9.673  -6.888  1.00  0.00           H
ATOM     49 2HG1 VAL     4      32.559   9.537  -8.638  1.00  0.00           H
ATOM     50 3HG1 VAL     4      32.799  11.120  -7.921  1.00  0.00           H
ATOM     51 1HG2 VAL     4      29.177   8.752  -7.361  1.00  0.00           H
ATOM     52 2HG2 VAL     4      30.510   8.264  -8.413  1.00  0.00           H
ATOM     53 3HG2 VAL     4      30.710   8.201  -6.643  1.00  0.00           H
ATOM     54  N   ALA     5      32.345  12.471  -6.211  1.00  0.00           N
ATOM     55  CA  ALA     5      33.021  13.765  -6.196  1.00  0.00           C
ATOM     56  C   ALA     5      34.521  13.592  -6.197  1.00  0.00           C
ATOM     57  O   ALA     5      35.052  12.475  -6.219  1.00  0.00           O
ATOM     58  CB  ALA     5      32.511  14.544  -4.971  1.00  0.00           C
ATOM     59  H   ALA     5      32.879  11.614  -6.220  1.00  0.00           H
ATOM     60  HA  ALA     5      32.753  14.314  -7.119  1.00  0.00           H
ATOM     61 1HB  ALA     5      31.415  14.694  -5.007  1.00  0.00           H
ATOM     62 2HB  ALA     5      32.734  14.020  -4.022  1.00  0.00           H
ATOM     63 3HB  ALA     5      32.967  15.549  -4.903  1.00  0.00           H
ATOM     64  N   LEU     6      35.271  14.641  -6.173  1.00  0.00           N
ATOM     65  CA  LEU     6      36.722  14.473  -6.173  1.00  0.00           C
ATOM     66  C   LEU     6      37.420  15.811  -6.143  1.00  0.00           C
ATOM     67  O   LEU     6      36.792  16.881  -6.122  1.00  0.00           O
ATOM     68  CB  LEU     6      37.174  13.638  -7.404  1.00  0.00           C
ATOM     69  CG  LEU     6      36.580  12.214  -7.580  1.00  0.00           C
ATOM     70  CD1 LEU     6      37.231  11.514  -8.782  1.00  0.00           C
ATOM     71  CD2 LEU     6      36.743  11.343  -6.323  1.00  0.00           C
ATOM     72  H   LEU     6      34.862  15.564  -6.154  1.00  0.00           H
ATOM     73  HA  LEU     6      37.013  13.950  -5.245  1.00  0.00           H
ATOM     74 2HB  LEU     6      38.279  13.552  -7.384  1.00  0.00           H
ATOM     75 3HB  LEU     6      36.958  14.216  -8.325  1.00  0.00           H
ATOM     76  HG  LEU     6      35.495  12.316  -7.792  1.00  0.00           H
ATOM     77 1HD1 LEU     6      37.097  12.092  -9.716  1.00  0.00           H
ATOM     78 2HD1 LEU     6      38.318  11.367  -8.642  1.00  0.00           H
ATOM     79 3HD1 LEU     6      36.787  10.518  -8.962  1.00  0.00           H
ATOM     80 1HD2 LEU     6      37.799  11.259  -6.005  1.00  0.00           H
ATOM     81 2HD2 LEU     6      36.175  11.759  -5.470  1.00  0.00           H
ATOM     82 3HD2 LEU     6      36.357  10.318  -6.476  1.00  0.00           H
TER
END

peptide_2_moved.pdb , same pdb with some just carbon alpha moved by hand :
ATOM      1  N   ALA     1      19.613   5.728  -6.281  1.00  0.00           N
ATOM      2  CA  ALA     1      20.289   7.022  -6.281  1.00  0.00           C
ATOM      3  C   ALA     1      21.789   6.848  -6.281  1.00  0.00           C
ATOM      4  O   ALA     1      22.320   5.731  -6.292  1.00  0.00           O
ATOM      5  CB  ALA     1      19.781   7.814  -5.064  1.00  0.00           C
ATOM      6  H   ALA     1      20.159   4.796  -6.281  1.00  0.00           H
ATOM      7  HA  ALA     1      20.021   7.561  -7.209  1.00  0.00           H
ATOM      8 1HB  ALA     1      18.685   7.964  -5.100  1.00  0.00           H
ATOM      9 2HB  ALA     1      20.005   7.300  -4.109  1.00  0.00           H
ATOM     10 3HB  ALA     1      20.237   8.819  -5.007  1.00  0.00           H
ATOM     11  N   PHE     2      22.540   7.897  -6.270  1.00  0.00           N
ATOM     12  CA  PHE     2      24.369   7.337  -6.515  1.00  0.00           C
ATOM     13  C   PHE     2      24.690   9.068  -6.255  1.00  0.00           C
ATOM     14  O   PHE     2      24.050  10.156  -6.244  1.00  0.00           O
ATOM     15  CB  PHE     2      24.437   6.942  -7.538  1.00  0.00           C
ATOM     16  CG  PHE     2      23.824   5.546  -7.729  1.00  0.00           C
ATOM     17  CD1 PHE     2      22.610   5.411  -8.413  1.00  0.00           C
ATOM     18  CD2 PHE     2      24.430   4.416  -7.174  1.00  0.00           C
ATOM     19  CE1 PHE     2      22.002   4.165  -8.523  1.00  0.00           C
ATOM     20  CE2 PHE     2      23.826   3.167  -7.294  1.00  0.00           C
ATOM     21  CZ  PHE     2      22.611   3.043  -7.966  1.00  0.00           C
ATOM     22  H   PHE     2      22.130   8.821  -6.260  1.00  0.00           H
ATOM     23  HA  PHE     2      24.287   7.177  -5.358  1.00  0.00           H
ATOM     24 2HB  PHE     2      25.541   6.842  -7.539  1.00  0.00           H
ATOM     25 3HB  PHE     2      24.233   7.549  -8.444  1.00  0.00           H
ATOM     26 1HD  PHE     2      22.116   6.283  -8.820  1.00  0.00           H
ATOM     27 2HD  PHE     2      25.362   4.509  -6.633  1.00  0.00           H
ATOM     28 1HE  PHE     2      21.050   4.077  -9.026  1.00  0.00           H
ATOM     29 2HE  PHE     2      24.295   2.298  -6.857  1.00  0.00           H
ATOM     30  HZ  PHE     2      22.134   2.079  -8.049  1.00  0.00           H
ATOM     31  N   GLY     3      25.980   9.099  -6.253  1.00  0.00           N
ATOM     32  CA  GLY     3      26.656  10.393  -6.239  1.00  0.00           C
ATOM     33  C   GLY     3      28.155  10.220  -6.239  1.00  0.00           C
ATOM     34  O   GLY     3      28.685   9.105  -6.251  1.00  0.00           O
ATOM     35  H   GLY     3      26.513   8.242  -6.261  1.00  0.00           H
ATOM     36 3HA  GLY     3      26.365  10.908  -5.371  0.00  0.00           H
ATOM     37  HA  GLY     3      26.378  10.953  -7.151  1.00  0.00           H
ATOM     38  N   VAL     4      28.906  11.269  -6.227  1.00  0.00           N
ATOM     39  CA  VAL     4      30.719  10.841  -6.509  1.00  0.00           C
ATOM     40  C   VAL     4      31.055  12.440  -6.212  1.00  0.00           C
ATOM     41  O   VAL     4      30.429  13.507  -6.201  1.00  0.00           O
ATOM     42  CB  VAL     4      30.798  10.251  -7.484  1.00  0.00           C
ATOM     43  CG1 VAL     4      32.326  10.136  -7.737  1.00  0.00           C
ATOM     44  CG2 VAL     4      30.276   8.794  -7.470  1.00  0.00           C
ATOM     45  H   VAL     4      28.497  12.192  -6.217  1.00  0.00           H
ATOM     46  HA  VAL     4      30.654  10.571  -5.305  1.00  0.00           H
ATOM     47  HB  VAL     4      30.366  10.744  -8.381  1.00  0.00           H
ATOM     48 1HG1 VAL     4      32.862   9.673  -6.888  1.00  0.00           H
ATOM     49 2HG1 VAL     4      32.559   9.537  -8.638  1.00  0.00           H
ATOM     50 3HG1 VAL     4      32.799  11.120  -7.921  1.00  0.00           H
ATOM     51 1HG2 VAL     4      29.177   8.752  -7.361  1.00  0.00           H
ATOM     52 2HG2 VAL     4      30.510   8.264  -8.413  1.00  0.00           H
ATOM     53 3HG2 VAL     4      30.710   8.201  -6.643  1.00  0.00           H
ATOM     54  N   ALA     5      32.345  12.471  -6.211  1.00  0.00           N
ATOM     55  CA  ALA     5      32.965  13.942  -6.049  1.00  0.00           C
ATOM     56  C   ALA     5      34.521  13.592  -6.197  1.00  0.00           C
ATOM     57  O   ALA     5      35.052  12.475  -6.219  1.00  0.00           O
ATOM     58  CB  ALA     5      32.511  14.544  -4.971  1.00  0.00           C
ATOM     59  H   ALA     5      32.879  11.614  -6.220  1.00  0.00           H
ATOM     60  HA  ALA     5      32.753  14.314  -7.119  1.00  0.00           H
ATOM     61 1HB  ALA     5      31.415  14.694  -5.007  1.00  0.00           H
ATOM     62 2HB  ALA     5      32.734  14.020  -4.022  1.00  0.00           H
ATOM     63 3HB  ALA     5      32.967  15.549  -4.903  1.00  0.00           H
ATOM     64  N   LEU     6      35.271  14.641  -6.173  1.00  0.00           N
ATOM     65  CA  LEU     6      37.139  14.337  -6.385  1.00  0.00           C
ATOM     66  C   LEU     6      37.420  15.811  -6.143  1.00  0.00           C
ATOM     67  O   LEU     6      36.792  16.881  -6.122  1.00  0.00           O
ATOM     68  CB  LEU     6      37.174  13.638  -7.404  1.00  0.00           C
ATOM     69  CG  LEU     6      36.580  12.214  -7.580  1.00  0.00           C
ATOM     70  CD1 LEU     6      37.231  11.514  -8.782  1.00  0.00           C
ATOM     71  CD2 LEU     6      36.743  11.343  -6.323  1.00  0.00           C
ATOM     72  H   LEU     6      34.862  15.564  -6.154  1.00  0.00           H
ATOM     73  HA  LEU     6      37.013  13.950  -5.245  1.00  0.00           H
ATOM     74 2HB  LEU     6      38.279  13.552  -7.384  1.00  0.00           H
ATOM     75 3HB  LEU     6      36.958  14.216  -8.325  1.00  0.00           H
ATOM     76  HG  LEU     6      35.495  12.316  -7.792  1.00  0.00           H
ATOM     77 1HD1 LEU     6      37.097  12.092  -9.716  1.00  0.00           H
ATOM     78 2HD1 LEU     6      38.318  11.367  -8.642  1.00  0.00           H
ATOM     79 3HD1 LEU     6      36.787  10.518  -8.962  1.00  0.00           H
ATOM     80 1HD2 LEU     6      37.799  11.259  -6.005  1.00  0.00           H
ATOM     81 2HD2 LEU     6      36.175  11.759  -5.470  1.00  0.00           H
ATOM     82 3HD2 LEU     6      36.357  10.318  -6.476  1.00  0.00           H
TER
END

code :
import pymol

import math

from pymol import (

                    cmd ,
                    stored
                    )

print('########## PYMOL VERSION ##########################################')
print('         ',  cmd.get_version() )
print('###################################################################')

pymol.finish_launching()

cmd.set('internal_gui_width' ,  '500')

cmd.load('peptide_1.pdb' , 'peptide_1')

cmd.load('peptide_2_moved.pdb' , 'peptide_2_moved')

diff_len = lambda x,y : math.sqrt((x[0]-y[0])*(x[0]-y[0]) + (x[1]-y[1])*(x[1]-y[1]) + (x[2]-y[2])*(x[2]-y[2]))

cmd.select('peptide_1_CA' ,'peptide_1 and name CA')

cmd.select('peptide_2_moved_CA' ,'peptide_2_moved and name CA')

stored.peptide_1_CA_coord = []

stored.peptide_2_moved_CA_coord = []

cmd.iterate_state('1' , 'peptide_1_CA' , 'stored.peptide_1_CA_coord.append((x,y,z))')

cmd.iterate_state('1' , 'peptide_2_moved_CA' , 'stored.peptide_2_moved_CA_coord.append((x,y,z))')

print('stored.peptide_1_CA_coord : ' , stored.peptide_1_CA_coord)

print('stored.peptide_2_moved_CA_coord : ' , stored.peptide_2_moved_CA_coord)

stored.newB = list(map(lambda x,y: diff_len(x,y), stored.peptide_2_moved_CA_coord, stored.peptide_1_CA_coord))

print('\nstorednewB to be passed : ', stored.newB , '\n')

cmd.alter('peptide_2_moved_CA', 'b=stored.newB.pop(0)')

cmd.save('peptide_2_moved_CA.pdb' , 'peptide_2_moved_CA')

cmd.spectrum('b', 'rainbow_rev' , 'peptide_2_moved_CA' , '0' , '1')

cmd.color('lightblue' , 'peptide_1' )

cmd.set('cartoon_transparency' , '0.5', 'peptide_1')

cmd.alphatoall('peptide_2_moved')

cmd.save('peptide_2_moved_CA_alphatoall.pdb' , 'peptide_2_moved')

cmd.load('peptide_2_moved_CA_alphatoall.pdb' , 'peptide_2_moved_CA_alphatoall')

cmd.spectrum('b', 'rainbow_rev' , 'peptide_2_moved_CA_alphatoall' , '0' , '1')

output:
########## PYMOL VERSION ##########################################
          ('2.3.0'
...
stored.peptide_1_CA_coord :  [(20.288999557495117, 7.021999835968018, -6.281000137329102), (23.989999771118164, 7.730000019073486, -6.269999980926514), (26.6560001373291, 10.392999649047852, -6.238999843597412), (30.356000900268555, 11.10099983215332, -6.228000164031982), (33.020999908447266, 13.765000343322754, -6.196000099182129), (36.72200012207031, 14.472999572753906, -6.172999858856201)]
stored.peptide_2_moved_CA_coord :  [(20.288999557495117, 7.021999835968018, -6.281000137329102), (24.368999481201172, 7.336999893188477, -6.514999866485596), (26.6560001373291, 10.392999649047852, -6.238999843597412), (30.7189998626709, 10.840999603271484, -6.508999824523926), (32.96500015258789, 13.942000389099121, -6.048999786376953), (37.138999938964844, 14.336999893188477, -6.385000228881836)]

storednewB to be passed :  [0.0, 0.5984269572074781, 0.0, 0.5275691186189471, 0.23679966390593804, 0.48716415818795433]

showing in pymol window:

that is the CAs trace of moved (rainbow colored) vs original (lightblue [lightgray out of ray to png]) according to Palette https://pymolwiki.org/index.php/Palette_Colorbars rainbow_rev :

that goes from red = 0 to BLUE = 1 as set out in
cmd.spectrum('b', 'rainbow_rev' , 'peptide_2_moved_CA' , '0' , '1')
againg transferred calculated CAs to all atom using AlphaToAll :
cmd.alphatoall('peptide_2_moved') (check it into code)
To use spectrum from min to max of the actual distance differences use in code above:
.....

stored.min = min(stored.newB)

stored.max = max(stored.newB)

print('\nmin of storednewB : ' , stored.min)

print('\nmax of storednewB : ' , stored.max)

print('\n')

cmd.alter('peptide_2_moved_CA', 'b=stored.newB.pop(0)')

cmd.save('peptide_2_moved_CA.pdb' , 'peptide_2_moved_CA')

cmd.spectrum('b', 'rainbow_rev' , 'peptide_2_moved_CA' , stored.min , stored.max)
....

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
Is there a DNAWeaver How-to guide?
Recently I've been trying to use DNA Weaver for customized gene assembly design.
Although there is a web version that allows to perform some assembly examples, I've tried to replicate them with python code.
There is some documentation about it and it the developers have provided some examples within the documentation. However, I wonder if is there any other more extensive documentation with all the possible parameters / attributes for each function / objects.
An example of this is the definition of a "fixed size overhangs" during Gibson Assembly design, instead of using a Tm range for this task.  It has something to do with the "Overhang selector"
overhang_selector=dw.TmSegmentSelector(min_tm=55, max_tm=70),

But I have not found any information about what would be the code to run to replace the "TmSegmentSelector" for "FixedSizeSegmentSelector". However there are no clear instructions about this.
Other example would be how to specify a custom - genome or DNA sequence from which regions could be PCR amplified as part of the routine assembly.
And so on...
Does anybody could provide some light about this?
Thanks in advance!
Jorge.


text:
Error of duplicated rownames although there are no duplicates
I have a data frame that I want to switch its row names from EnsembleID (GENEID) to gene symbol (SYMBOL). When I try to switch, I get this error:
Error in `.rowNamesDF<-`(x, value = value) :
  duplicate 'row.names' are not allowed
In addition: Warning message:
 Error in `.rowNamesDF<-`(x, value = value) :
  duplicate 'row.names' are not allowed

The code I'm using works for other similar data frames. Only this one is giving me trouble. There are no duplicates in the row names, I checked, so the source of this error is strange.
This is the code:
GetSymbols <- function(df) {   # Switch from geneID format to Symbol format

  ensembl.genes = as.vector(rownames(df))
  geneIDs = ensembldb::select(EnsDb.Hsapiens.v86, keys= ensembl.genes, keytype = "GENEID", columns = c("SYMBOL","GENEID"))
  dupli = duplicated(geneIDs$GENEID)
  geneIDs = geneIDs[!dupli,]
  rownames(geneIDs) = geneIDs$GENEID
  rownames(df) = geneIDs$SYMBOL
  return(df)

}

#Calling the function:
 df = GetSymbols(df)

A sample of the dataframe:
structure(list(logFC = c(0.362414024114299, 0.270660335317552,
0.250197714648158, 0.355106754020497, 0.860436247608723, 0.331838240496886,
0.642000001979217, -0.506238094622956, 0.199130048525963, 0.226010606161764,
-0.300273572714138, 0.763447672404514, -0.282220097838676, 0.686284440644756,
0.209266959343866, 0.201216572039244, 0.467207891400503, 0.207476905368996,
0.548263917432251, 0.582095719576479, 0.167369145307915, -0.239886539805618,
-0.133088162158502, 0.432528267703316, -0.232843401197013), AveExpr = c(2.63663927914016,
6.23730774047258, 6.74130527553022, 6.51256660562955, 3.51386518575641,
0.962682352871946, 3.60110717340049, -1.07537043066235, 4.55672701732791,
3.96125159652725, 1.20473881401599, 1.72955742088622, 2.10495435034315,
2.95446094011986, 4.31205976523475, 4.85412008885978, 4.09171246258306,
3.94681319329962, 6.27193781389662, 0.936821016422818, 7.97270280545117,
2.4470079966345, 5.75991260844846, 3.84225682530045, 3.35515997418382
), t = c(5.15993300980101, 4.74644046948475, 4.67895886939685,
4.65270476519835, 4.39369295809775, 4.51083389626156, 4.20489542862464,
-4.49987927160777, 4.1096966271885, 4.03273843222089, -4.09143780518073,
4.07713970281633, -3.99157698713235, 3.97814493972727, 3.94402861982903,
3.92673590933521, 3.92436825669156, 3.90538427413924, 3.89364498564491,
3.93360332510695, 3.80233390398449, -3.76935782400013, -3.7013905266149,
3.68313224298669, -3.68114307289328), P.Value = c(4.50582660244125e-07,
3.21245260466636e-06, 4.37260426507432e-06, 4.92523952290176e-06,
1.54869545509172e-05, 9.2842399715271e-06, 3.45373717288344e-05,
9.74370585569957e-06, 5.12014322967072e-05, 7.00192255628614e-05,
5.51721193906694e-05, 5.84844087520547e-05, 8.26184479488647e-05,
8.71763749953859e-05, 9.9850615705057e-05, 0.000106924308803713,
0.000107929018751166, 0.000116314737750083, 0.000121805424073131,
0.000104060063751203, 0.000173710084683786, 0.000197140244520663,
0.000255164374909145, 0.000273300166932535, 0.000275347732934516
), adj.P.Val = c(0.00727510763230165, 0.0198807293341929, 0.0198807293341929,
0.0198807293341929, 0.0357217668827299, 0.0262203124576875, 0.06970505049172,
0.0262203124576875, 0.0858444785191522, 0.09421086799483, 0.0858444785191522,
0.0858444785191522, 0.0968123298197961, 0.0968123298197961, 0.0968123298197961,
0.0968123298197961, 0.0968123298197961, 0.098333518854239, 0.098333518854239,
0.0968123298197961, 0.130401412989609, 0.138392451653505, 0.150726778618736,
0.150726778618736, 0.150726778618736), B = c(5.59649208873277,
4.23844568248881, 3.95926511142815, 3.85011503575047, 2.70095619489428,
2.52151007226572, 2.02920447371096, 1.82077681362955, 1.73104697407381,
1.44381690648743, 1.32264026808754, 1.26349133643967, 1.17476645994761,
1.17423988785926, 1.13844103561967, 1.07813935334313, 1.06670248721183,
1.0004861785984, 0.949862540556422, 0.634079467108277, 0.620590447127368,
0.489071423128816, 0.29054426889472, 0.254833988789798, 0.246174538031329
)), row.names = c("ENSG00000188938", "ENSG00000182768", "ENSG00000174579",
"ENSG00000119523", "ENSG00000271798", "ENSG00000204923", "ENSG00000281910",
"ENSG00000271092", "ENSG00000153767", "ENSG00000116903", "ENSG00000168101",
"ENSG00000206834", "ENSG00000180035", "ENSG00000251733", "ENSG00000058729",
"ENSG00000132467", "ENSG00000212464", "ENSG00000115561", "ENSG00000230669",
"ENSG00000206612", "ENSG00000275272", "ENSG00000162139", "ENSG00000143889",
"ENSG00000207233", "ENSG00000119878"), class = "data.frame")

Thanks very much! This is extremely helpful when doing immune deconvolution. I used quanTIseq and it said my row names are duplicated while all of my row names are unique. However, after removing all the ids with the duplicated() function, the immune deconvolution worked! May I know what magic duplicated() do?

I found an answer shortly after posting this question:
ensembl.genes <- rownames(df)
geneIDs <- ensembldb::select(EnsDb.Hsapiens.v86, keys= ensembl.genes, keytype = "GENEID", columns = c("SYMBOL","GENEID"))
dupli = duplicated(geneIDs$SYMBOL)
geneIDs = geneIDs[!dupli,]
ind = intersect(rownames(df), geneIDs$GENEID)
df = df[ind,]
rownames(df) = geneIDs$SYMBOL

Can you please mark this as an accepted answer if it works for you?


text:
Cellranger results have too many cells
I have the results of cellranger analysis for single-nucleus RNA seq data that was done by someone else. So I do not know which parameters were used for cellranger. In the results, there are 77000 estimated cells (which is way too high) and around 800 median reads per cell. Those are filtered counts. Raw feature_bc_matrix has almost 2 million cells.

How to tune cellranger count to get lower number of cells?

Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.

Do you have the web_summary.html file? Then you could add the barcode rank plot (top right) to your question. To me it looks as if you have a lot of background and the cell count estimation did not work well.

yes, I added it. Thank you. Is that a library preparation issue then? I am planning to use cellbender to remove ambient signal.

The Barcode Rank plot shows very high background in your sample.
The cell estimation from Cell Ranger will look for a sudden drop in the number of UMIs and use this to call cells. In your case, the drop is too much to the right because the empty droplet plateau is almost completely merged with the real cells. If you have already looked at CellBender your case best corresponds to the rightmost example image.
With high background of free mRNA, even empty droplets will contain a significant number of mRNA molecules and become hard to distinguish from real cells.
This likely is a consequence of the single nulcei preparation. To isolate the nuclei, the cells necessarily need to be broken and all the cytoplasmic mRNA is freed contributing to the background. You could try removing this background in future experiments by increasing the number of washing steps after cell lysis.
Using software such as CellBender, SoupX or DropletUtils may help with identifying and reducing the background. CellBender also will rerun the cell estimation step, automatically adjusting the number of called cells.
In any case you can manually reduce the number of cells by filtering based on the minimum number of UMIs or genes. However, the higher the background the more arbitrary such a thresholding strategy will become.

thank you. CellBender did not really help in my case, it returned very few cells with high % of mitochondrial genes. As for manual reduction, I tried doing it on CreateSeuratObject step using Seurat R package. However, the vast majority of filtered cells have less than 2000 features. Is low UMI also consequence of nuclei preparation?

For single nuclei you can increase the number of mapped reads by including intronic reads in the cell ranger call (--include-introns, this is the standard configuration in the latest version of cell ranger). However, in case of high background much of your sequencing depth goes towards empty droplets and your cells wind up less well covered. I think you will have make the most out of the low quality you have. Best of luck.


text:
Are the doublets in the tutorial dataset in Scanpy/ Seurat already filtered?
I noticed the tutorials that Scanpy and Seurat use do not demonstrate doublet removal in their down stream analysis.
Is the dataset output of cellranger count already doublet removed or do I need to incorporate doublet removal before doing quality control etc?

Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.

CellRanger doesn't remove doublets. Also, there is no doublet remotion in the tutorials of Scanpy, if you want to apply doublet filtering you should use sc.external.pp.scrublet(). I'm not sure about Seurat.

No in general cellranger does not remove doublets. You can apply a tool like Scrublet in scanpy or Doubletfinder in Seurat to infer doublets based on a mixed-transcriptional signal.
I say in general because cellranger does detect certain types of doublets in two contexts:

Multi-species experiments, e.g. the so-called barnyard experiment, cellranger detects cross-species doublets
With CellPlex sample multiplexing, cellranger detects cross-tag (cross-CMO) doublets


text:
Why my Seurat object has gene name as "gene-number"
I tried to analyze single-cell RNA sequencing data from PMID: 32849601. I downloaded fastq files. I also made a canine genome reference from https://www.ncbi.nlm.nih.gov/assembly/GCF_000002285.3/ according to a guideline from cellranger (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/advanced/references#mkref-intro). I ran cellranger count as usual and created Seurat objects using features, barcodes, and matrix files based on the Seurat tutorial without modification.
I found all gene name has the suffix gene "-4" as below. [![enter image description here][1]][1].
Which step should I correct to have a plain gene name without the suffix? Since I have never found this issue before, it's really interesting. Is this related to the step of making a reference genome?
[1]: https://i.stack.imgur.com/oF1oi.png


text:
Production database system for storing vcf records
I’m standing up a production bioinformatics pipeline that’ll be producing standard VCF files. I’d like to store this data in an SQL database that ingest the VCFs and allow me and my team to easily query and visualize many VCFs. Because multiple people will have access to the data, roles/permissions need to be built in. I don’t want to create this system from scratch, but it needs to be both robust and intuitive. Are there any products like this available today?
[Edit]: The answers provided to this question are on the right track, but only provide a small slice of the functionality I'm looking for. For starters, I'm looking for a hosted/managed solution; something potentially like Basespace, but more user-friendly and not quite so bloated.
Additionally, the linked solutions are mostly about getting vcfs into a db, but don't make much headway into providing exploration/visualization tooling.

May be something useful here https://bioinformatics.stackexchange.com/questions/15248/how-to-import-large-bed-gff-vcf-paf-sam-files-into-an-sql-database

Hi @JessimeKirk IMO it is better to ask a question focussed on a single specific issue in most cases. Maybe summarise the answer kindly provided above and focus on a Basespace type solution?

If you are open to non-free solutions, check out VarSome Clinical. (disclaimer: I work for the company behind this, so I am obviously not impartial, but it really is good, honest!).

Like terdon says, you may have to go for commercial solutions if you want layman storage/visualization for VCF files. You're looking to offload bioinformatics work and no free tool can effectively replace something a human being needs to do


text:
Why can't AlphaFold predict the consequences of point-mutations?
In the literature, it specifically states that AlphaFold has "Has not been trained to predict structural consequences of point mutations". See : https://alphafold.com/faq

AlphaFold has not been validated for predicting the effect of mutations. In particular, AlphaFold is not expected to produce an unfolded protein structure given a sequence containing a destabilising point mutation

What I want to understand, is why?
If I change one amino acid, and it's not destabilising, why can't AlphaFold use its training and form a moderately confident structure? I mean, to a computer, what's the difference between a wild-type and a mutated protein? I know what you may be thinking, why don't you just try it yourself, and see if AlphaFold works. But if I don't have the experimental structure of the mutant, how on earth am I supposed to believe that this is even close to the theoretical real thing?
Maybe I'm not understanding the main premise, but it's something that's been bugging me for a while now. Any insight into this would be much appreciated.

What happens if you mutate a Gly to Pro ??

This is a good point. I have tried this in fact, and obtained the same structure. Bizarre.

Cmon Gly to Pro where Pro can't assume Gly φ ? @jambajuice

Other have addressed why AlphaFold2 cannot really get the necessary signal from MSA (Multiple Sequence Alignment) and memory to properly model a variant, but I thought I'd add my observations/rants.
I have modelled in silico both engineering and clinical variants in the past, using PyRosetta, MD and other tools. For me, AlphaFold2 has really stood out but in a different way than the hype.
Variants can have complicated effects
I developed a tool Venus, https://venus.sgc.ox.ac.uk/, which collates different pieces of information to help figure out the effect a variant was having beyond a simple ∆∆G calculation. This is because often a variant is having a variety of effects, for example a stable but catalytically dead variant can still form a complex and sequester a critical component (e.g. in ref POLR2E-sequestering POR2A is more pathogenic than a simple truncation). This is not only true with human clinical variants, but also with engineering variants: for one protein I have worked on, DogCatcher, lowering the isoelectric point away from neutral pH was critical, not the stabilisation of the transition state (ref). Another example is that the entrance loops in cytochromes drive selectivity more so than the active site configuration.
The unknowns: alt conformers and complexes
I will be the first to admit that most folk want a deleterious ∆∆G value and will p-value shop for it and not catering for this is a bad idea for a publication. But in terms of mechanism, the biggest issue I face are dynamic processes involving a residue where the other active/inactive conformer is unknown or when the residue is a surface residue possible at the interface in a complex, which is known simply to exist from a coIP western. No nearby gnomAD variants or presence of post-translational sites or sequence conservation do show that something may be important but not why.
Enter AlphaFold2
AlphaFold2, via the more user-friendly ColabFold, allow the creation of wild type monomers, homo-oligomers, but also hetero-oligomers (complexes) and alternative conformations.
These days I seen a cartoon of an interaction and I model it in quintuplicate. Sometimes nothing binds, but sometimes a complex is found 5 out of 5. The weirdest part is that it reveals some novel mechanism that makes sense that has probably never been seen before, but is beyond the scope of the experiment so one has the power to just put them aside —interfaces which are stronger phosphorylated, inhibitory domains binding in the same site but in a different way, unexpected complex arrangements etc. So the true revolutionary power of AlphaFold2 is predicting complexes and alternative conformations, which can be used to predict the mechanism of a variant.
WIP
Complexes do not always resolve and there is a lot of discussion in terms of massaging the MSA —e.g. boney-fish+tetrapods only etc. And there is very little cross-linking mass-spec data available for validation and/or constraining. But hopefully things will get better.
Footnote
The last paragraph sounds like an exaggeration, so I best give an example. Here is an example for the drawer from today. A target protein (catalytic domain = pale) has a regulatory terminal which inhibits in isolation (terminus = turquoise), however, when modelled with another protein (mustard) this terminal (terminus = coral) is flipped away. Thus revealing the mechanism by which the mustard protein sequesters the regulatory helix (coral), which would otherwise (turquoise) inhibit the catalytic domain (pale cyan).  In the literature, this is a western blot. Where a variant of interest involved in this mechanism one state may be more energetically favourable by scoring the ∆G of the different "snapshots" (=static conformers).

What is MSA?? I dont get the footnote, I know I am dumb but whould it  be easy to reformulate for dumb people ?

This is a detailed answer, but I feel like it doesn't address the question at hand.

This answer is from common knowledge rather than specific infield expertise. The question could be more expertly answered by other members.
If you really want to pursue this type of calculation, it's possible that something like INPS-MD de novo -> protein structure stability from variants will provide an answer. So maybe compare the outputs with AlphaFold?
Intuitively, I don't think it's cool, just my opinion. In my opinion, an experimentally derived structure is required as the basis for your calculation, where you want to consider the impact of an amino acid mutation.
It's important to separate de novo prediction from variant prediction. Alphafold is de novo prediction. What you are looking at is structure stability from a variant (see Summary). That's my core answer, these different calculations. To explain this more clearly ...
Good variant stability calculators are DUET (2014) and INPS-MD (2016), amongst others. Just to reiterate, a good starting point is an experimentally derived protein structure.
The webservers are here:

http://structure.bioc.cam.ac.uk/duet
https://inpsmd.biocomp.unibo.it/inpsSuite

I personally don't know Alphafold but understand its predecessors which accommodate convolutions by leveraging deep-learning. The basis of the predecessor's calculations firstly are de novo prediction and secondly can miss the impact of variants if they are not directly involved in the core-algorithm. It's about identifying which amino acids are interacting considering all available homologous amino acids within a database. The calculation can predict local interactions with extreme accuracy, but it's a prediction. It will also depend on whether there are single amino acid changes or two- or more amino acid changes (they could compensate each other, particularly for a natural variant).
In contrast, DUET and INPS-MD, amongst a lot of other similar programs, consider the property shift of an individual amino acid in relation to its immediate local stereo-chemical environment measured by a shift of free-energy.
Essentially, these are two sets of very different calculations.
Summary The problem I intuitively see is if de novo prediction in any form is used, okay they are very good, but they're just predictions. Variant structural predictions are also just predictions  which are very widely respected. Stacking de novo prediction onto predicting protein structure stability resulting from variants has scope for error, especially if there was some hidden non-linear effect.
There could be additional issues because the de novo calculations immediately prior to Alphafold don't predict the precise local distances.

AlphaFold would simply respond

'it's not been trained'

They'd want a wet-lab experimental dataset with wild-type vs. variants, if these exist with sufficient abundance to prevent over-training. It would be a very skewed dataset so of limited application in my personal opinion.

As marcin points out in the comments: by "AphaFold (AF) has not been trained to predict structural consequences of point mutations" it is meant "AF is not able to tell you whether your point mutation is destabilizing or not, and if it is destabilizing then the structure it predicts will most probably be wrong".
It is totally possible that AF predicts the structure of a protein well when only non destabilizing mutations are introduced. As pointed out in the paragraph marcin quotes, however: "AlphaFold has not been validated for predicting the effect of mutations." And as long as we don't know, we should be warry of any of AlphaFold predictions with point mutations. Maybe it works, maybe it doesn't, or maybe it's case dependent.


text:
Applying colocalization analysis in Asian populations
I am planning to add colocalization method as a post-GWAS analysis in my study which is solely based on east Asian populations. I have noticed that coloc, enloc and eCaviar are being commonly used for colocalization analysis but all of which are being conducted in European GWAS or European dominant meta-analyses. Some stated that GTEx eQTL data, which might be a part of requirements in these tools, was mainly generated from European populations, and thus, it would be difficult to get the accurate results in Asian populations because of LD differences. So, if anybody who had similar experiences or who did colocalization methods in Asian GWAS is here, please let me know how to deal with this.


text:
SNPs with high population differentiation from 1k Genome dataset
I am trying to reproduce the results from this paper "Human genomic regions with exceptionally high levels of population differentiation identified from 911 whole-genome sequences".
Specifically I want to extract  high differentiated  ( or less) SNPs from 1k Genome data set representing high proportion of the population present.
The paper mentioned using Vcffixup from Vcflib (https://github.com/vcflib/vcflib) but there might still be other things I am missing.

Update 2 (MWE)

So this is my code snippet.
## to dowload datafile, from linux terminal, use curl or wget
curl -O https://1000genomes.s3.amazonaws.com/release/20110521/ALL.chr22.phase1_release_v3.20101123.snps_indels_svs.genotypes.vcf.gz
### unzip the file
gunzip dowloaded file
### take the first few lines from the vcf file.
### since actual file is huge, the name of sfile for testing is
### myfile.vcf
head -200 downloadedfile > myfile.vcf

Now the next section was done in python environment (jupyter notebook).
## using the library pysam
from pysam import VariantFile
import pandas as pd
with VariantFile(test_datafile) as vcf_reader:
for record in vcf_reader:
    sample_file = record.samples.keys()
    alleles = [record.samples[x].allele_indices for x in record.samples]
    break ## just to make sure my data is as intended

Now to the VCF tools part - vcffixup was used in the paper.
## Install vcflib
   sudo apt-get install libvcflib-tools libvcflib-dev
### export path
   export PATH=$PATH:/usr/lib/vcflib/bin
### refresh enviroment variables
   source ~/.bashrc
## run vcffixup
  vcffixup myfile.vcf

A sample of vcffixup output is hown below:

So the questions questions I have is:

how to compute derived allele frequency differences at all variants for pairs of continent and pairs of population within continent

Thanks!

Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.

Thanks for the edit! But how do we choose the snps? And what do you mean by present? Present in which sample of the multi sample vcf file? And do you care about zygosity? I mean, the vcf is already what you want:it has 0/0 when a variant is absent.


text:
GSEA. Subsetting Gene Ontology, Biological Processes, by gene set families of interest
I have run GSEA - on Gene Ontology , Biological Processes (GO and BP hereafter) - on some Mouse bulk RNA-seq data, and am currently in the process of going through the results. To do this, I have used the library fgsea in the R programming environment.
Prior to running the GSEA, in an attempt to refine the results (many many significant gene sets were found), I wish to exclude particular categories in the GO BP tree of gene sets - see the following link: http://www.informatics.jax.org/vocab/gene_ontology/GO:0008150.
For the purpose of this question, lets assume I would like to exclude all gene sets which fall under the two categories: pigmentation and reproduction. Is there any way in which I can obtain all of the genesets under these two categories, in order to later subset the .gmt file, prior to running fgsea ? Essentially, what I mean is, without going one by one and taking down hundreds of gene sets by hand. Is there a resource which contains this hierarchy of GO BP genesets as some sort of downloadable data structure?
Thanks.


text:
Lower case vs. upper case nucleotids in sequence vs. dots at the end
What is the difference between lower case and upper case nucleotides in a sequence? My other question is what are the dots at the end of the sequence? Some examples are shown below:

GGgG,GGgG,GGgG,GGgG,GGGG,GGGG…
GGGG,GGGG,GGGG,GGGG,GGGG,GGGG…
GGGG,gggg,GGGG,GGGG,UUUU
ggGg,ggGg,ggGg,GGgG,GGgG,GGgG…

These sequences are real experimental samples. I obtained them from QuadBase. Here is the link below:
https://quadbase.igib.res.in/
Hopefully you will provide some literature within the thread in order to understand this concept in bioinformatics.
BTW I'm a beginner in this field and hopefully this
Bioinformatics Stack Exchange site will help me find out the answers.

Please [edit] your question and tell us exactly how you got these sequences. You have linked to a page with various tools, which one gave you this output? What was your input? The meaning of upper vs lower case can vary enormously depending on context. for example, in genomic sequences, lowercase letters often mean low complexity regions, but we need to know more about these specific sequences and how you got them to be able to help you. Have you looked at the quadbase paper? http://nar.oxfordjournals.org/content/early/2016/05/16/nar.gkw425.full

GGGG,gggg,GGGG,GGGG,UUUU

Quadbase appears to be about DNA secondary structures, albeit the above example is RNA. The website states,

G-Quadruplexes or G4s are secondary DNA structures that are now established to have functionally crucial roles in various cellular processes.

Two guesses, firstly its all just a bit weird, secondly its a representation of hypoxanthine. Its more likely to be the second guess than the first.

The nomenclature is a bit weird: gggg with UUUU would infer non-canonical binding/bounding in WUSS (Washington University Secondary Structure) format for a psuedoknot  (type of secondary structure). If that wasn't the interpretation it would be kinda weird to share nomenclature with a major secondary structure format (WUSS) for secondary structure notation.

The 'dots', i.e. '.' appear to represent continuation, although in dot-bracket format (secondary structure format) this represents a nucleotide that doesn't hybridise.

Its worth reading the paper for hypoxanthine representation (normally its 'I'), because 'I' will behave like a guanine residue and would then make sense if it was being represented as 'g'. Thus, the following means no hypoxanthine,

GGGG,GGGG,GGGG,GGGG,GGGG,GGGG…

My bet is its point 2, otherwise it doesn't make sense.


text:
Building database for MetaCache takes extremely long
A question to those who have experience with MetaCache (metagenomic tool):
I have tried to build the MetaCache taxonomy from refseq (only bacteria), as described in the EXAMPLES here. However, the process turned out to be excruciatingly long, and I eventually gave up waiting after a week, reaching the progress of only 45%. I am using a 32-core server and the MetaCache seemed to make use of at least 16 cores, so I am puzzled by such slowness.
Is this normal behavior? Are there ways to do this faster or pre-built (relatively recent) databases? Is there a specific MetaCache forum, where I could pose a question to the developers?

MetaCache is a classifier in the Kraken mould. Here's the blurb.

Metagenomic shotgun sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments.

...

As a result, MetaCache consumes significantly less memory compared to the state-of-the-art read classifiers Kraken and CLARK while achieving highly competitive sensitivity and precision at comparable speed.

I don't use MetaCache but do other things that take days for building. While you note how many cores you have you did not note how much memory you have. The instructions note with default settings your system should have around 64GB of RAM Also they probably used as SSD drive, an HDD drive will take much longer.

Maybe state the database size and RAM as the MetaCache abstract states,

"For example, using NCBI RefSeq draft and completed genomes with a total length of around 140 billion bases as reference, MetaCache’s database consumes only 62 GB of memory while both Kraken and CLARK fail to construct their respective databases on a workstation with 512 GB RAM."

Perhaps consider Kraken2?

@M__ Kraken2+Bracken work fine (perhaps because I am using a pre-existing downloaded index). I am interested specifically in MetaCache.


text:
PyMol alignment script
Is there a way write a script in python to use "super" to align proteins from a 2D array? Each row of the array has 2 columns, and for each row, I want to "super" the two proteins (one in each column)

Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.

What do you mean by super? Is it this?

You can use Python "normally" and import the pymol module. In the script that holds the proteins in your matrix, you can add a function that does the super alignment.
To use it, in your console type pymol -cq this_script_name.py.
You need to have pymol installed and accessed via console. Easiest way I found to manage it is to create a new environment via conda and install pymol there.
import pymol

def super_prots(prot_1, prot_2):
    pymol.cmd.fetch(prot_1, 'prot_1')
    pymol.cmd.fetch(prot_2, 'prot_2')

    # If you are using already downloaded pdb files, use load instead:
    # pymol.cmd.load(prot_1, 'prot_1')
    # pymol.cmd.load(prot_2, 'prot_2')

    pymol.cmd.hide('all')
    pymol.cmd.set('ray_opaque_background', 0)

    # Show cartoon and paint them different
    pymol.cmd.show('cartoon', 'prot_1')
    pymol.cmd.color('orange', 'prot_1')
    pymol.cmd.show('cartoon', 'prot_2')
    pymol.cmd.color('lightblue', 'prot_2')

    # Align both proteins
    pymol.cmd.super('prot_1', 'prot_2')

    # Improve image quality, and get png.
    pymol.cmd.set('depth_cue', 0)
    pymol.cmd.set('spec_reflect', 0)
    pymol.cmd.set('cartoon_sampling', 15)
    pymol.cmd.set('ribbon_sampling', 15)
    pymol.cmd.set('antialias', 2)
    pymol.cmd.space('cmyk')

    # This creates high-quality images, but might take a long time to
    # process. Adjust parameters accordingly.
    pymol.cmd.png(f'{prot_1}_{prot_2}.png',
                  width=2000,
                  dpi=300,
                  ray=1
                  )
    pymol.cmd.reinitialize()

if __name__ == '__main__':
    # These are entries from PDB. It can work with already downloaded
    # files, but you have to change the function accordingly
    protein_matrix = [['1EWL', '4W5B'],
                      ['4E2D', '3ATZ'],
                      ]
    for prot_1, prot_2 in protein_matrix:
        super_prots(prot_1, prot_2)

This will generate .png images of the alignment, but you can also ask it to save the alignment into .pdb files adding pymol.cmd.save(f'{prot_1}_{prot_2}.pdb')
The commands' documentation can be found here.


text:
CLI access to genomes/proteomes by BioSample ID
This question was also asked on Biostars
I'd like to download multiple genome assemblies or proteomes using a set of BioSample IDs from NCBI.
I'm able to find the assemblies belonging to the BioSample IDS in a browser (in the search field of https://www.ncbi.nlm.nih.gov/), but couldn't find a commandline interface solution (CLI).
E.g. for BioSample SAMN09405588 the assembly id is PDT000806148.1, and from https://www.ncbi.nlm.nih.gov/assembly/GCA_014136285.1/ I can download the proteome: GCA_014136285.1_PDT000806148.1_protein.faa.gz
Thank you for your help!

I think this question is not a duplicate of https://bioinformatics.stackexchange.com/questions/15794/using-entrez-efetch-to-retrive-fasta-file-from-any-ncbi-database - there a gene id is given, here a biosample id.

Answer from @m, converted from comment:
Biostars are recommending esearch ... its not the approach I'd have taken but presume will do the job. Its available via SRA, so I'd have gone for the SRA toolkit. Is SRA more appropriate in context? (I liked Biostars BeautifulSoup solution)
[Please update or improve this answer if you can]


text:
How to analyze Circular Dichroism data? Best practices, typical approaches and so on
I have to analyze circular dichroism data of proteins and nucleic acids for my PhD. Despite I have been able to find vast literature that helps to interpret a spectrum, most of the papers describe only the experiment as is, so telling what the methods to acquire a good set of spectra were, using JASCO technology or Synchrotron Radiation Circular Dichroism (SRCD), and the final interpretation of the plot.
Basically, I come from biomedical signal analysis and processing. So I was thinking that extracting features from spectra could be kind of similar to extracting features from an ECG signal for example.
Given that I don't precisely know what the experts do with cd spectra, If you have any advice could be of great value to me.

I'm not exactly clear about the question or the project. Are ECG's electro-cardiograms? Interpreting ECG's you'd have thought would be applicable to spectral analysis (its obvious). However, I believe a cardiologist is better positioned to identify aberrations and ECG spectral analysis is certainly not mainstream if done at all (I think the latter is correct).

https://pubs.rsc.org/en/content/articlehtml/2021/cs/d0cs00558d --> Tools and methods for circular dichroism spectroscopy of proteins: a tutorial review

take two different protein that fold in the most similar ways and get their structure then calculate both CD spetra ? calculate all the predicted CD spectra of all the alfa-fold predicted structure and figure out if you can given a CD spectra get the name of the protein ?? play with the data using AI to figure out pattern (i.e. percentage of alpha elical vs betha sheets and so on ...


text:
Experimental data on searching for breast cancer
In addition to the Wisconsin breast cancer (diagnosis) can be found in UCI machine learning library for breast cancer datasets, what websites can you find similar breast cancer datasets?Or which websites can provide free breast cancer data sets?

Hi @Redbean your question doesn’t appear to be about R, but about datasets, hence why I removed the tag.


text:
How to remove black outline from Seurat's VlnPlot violin plot
Using Seurat's VlnPlot, how can I remove the black outline around the violin plot?
For example, how can I change from the following graph with a (black) outline:
    VlnPlot(ilc2,
        features = "gene",
        split.by = "group",
        cols = c("#198BFD", "#EC6464"),
        pt.size = 0) +
  geom_boxplot(width = 0.1) + theme(aspect.ratio = 1)

To a graph like the one below, which does not have an outline around the violin plot:

Please consider "accepting" your answer and upvoting the other helpful answer here.

wish there was a more subtle answer, but this works.  Place this previous to your VlnPlot
update_geom_defaults("violin", aes(linewidth = 0))

vln_df = data.frame(gene = ilc2_bs[["RNA"]]@data[ "gene", ],
                    group = ilc2$group)

ggplot(vln_df, aes(x = group,
                   y = gene, fill = group)) +
  geom_violin(aes(fill = group,
                  color = gene),
              color = NA) +
  scale_fill_manual(values = c("#619CFF", "#F8766D")) +
  geom_boxplot(aes(fill = group),
               width = 0.1) + theme(aspect.ratio = 1)


text:
Genome-guided transcriptome reconstruction: should I filter my reference annotation by transcript_support_level or other tags?
Is there really any advantage to filtering the GTF annotation for transcriptome reconstruction (or, for example, for pseudo-alignment quantification)? Are there any downsides (i.e. reasons why I should NOT do that)?
I'm going to run a transcriptome reconstruction with StringTie on several RNA-seq samples, and I want to be stringent in the detection of novel isoforms.
To do that, I'm thinking of filtering the reference annotation that I provide to StringTie, removing transcripts that are not well supported by strong evidences. I found that the GENCODE tag "transcript_support_level" could be what I should filter for.
However, I couldn't find anybody suggesting to filter the GENCODE reference annotation by the transcript_support_level tag.
From the GENCODE documentation:
transcript_support_level    {1,2,3,4,5,NA} [0,1]
transcripts are scored according to how well mRNA and EST alignments match over its full length:
1 (all splice junctions of the transcript are supported by at least one non-suspect mRNA),
2 (the best supporting mRNA is flagged as suspect or the support is from multiple ESTs),
3 (the only support is from a single EST),
4 (the best supporting EST is flagged as suspect),
5 (no single transcript supports the model structure),
NA (the transcript was not analyzed)

I ran a test on GENCODE v40, and it seems that the cumulative distribution of genes from support level 1 to NA is:
SupportLevel | # genes
           1 | 19311
           2 | 23680
           3 | 28212
           4 | 29429
           5 | 31566
          NA | 61552

So it seems that I would miss out many genes from the reference annotation if I filter by, let's say, transcript_support_level <= 2
Couldn't find any info about advantages or disadvantages... apparently most people simply use the comprehensive GENCODE annotation with StringTie or Salmon or other tools like that, but I also know that these approaches generates many false detections that needs to be filtered downstream.
Hopefully I'm not the first person (over)thinking this :)


text:
Select representetive sequence within MSA (protein)
I am trying to pick the best representative sequence from an amino acid MSA. I do not want to use the consensus, as it generates an artificial one. Here is my approach:

I calculate a Percentage Identity Matrix (PIM) from the MSA file
I check the highest value in each column and rank the respective
sequences. What I mean by this, for example, the values in the first
column would be the Percentage Identity of the first seq compared
the rest of the dataset. If I pick the highest value in there, that
would give me the sequences that are highly identical to the first
seq.
I do this for the every column and rank the sequences. The top
ranked sequence would yield me the best representetive sequence
within my MSA data set.

The problem with this approach is that its computationally expensive to calculate the PIM as its doing a character match, comparing each sequence's position with all of the dataset. Is there any easier way to achieve this? Im not sure, if we could achieve something similar by looking at the tree. I think we can find the lowest common ancestor by the tree but I do not think that would yield a representetive sequence.

Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.

Can you give more context on why you want a representative sequence and why a consensus sequence is not appropriate? What is your use case?

I am sorry for late reply, I am in the process of relocating to a different country. We are trying to pick a reference sequence when a reference seq does not exist. Later we will be doing some kind of variant/diversity analysis and we will be needing to map the Highly Convserved Regions to this selected reference.

I have two suggestions that I used in the past (both using Python/BioPython though). They are arguably the same, but obtained through different paths.

Calculate the (artificial) consensus sequence of your alignment. Then, compare each sequence in your MSA to the consensus in a pairwise alignment, keeping the score. The highest scoring real sequence will be your representative.

from Bio import AlignIO
from Bio import pairwise2
from collections import Counter

aln_file = "MSA.aln"
alignment = AlignIO.read(aln_file, "clustal") # whatever format you have

for i in range(0,alignment.get_alignment_length()):  # iterate position by position
    aa_position = []

    # Get all residues in that position
    for record in alignment:
        aa_position.append(record.seq[i])

     # Use the most common residue as representative
    consensus_list.append(Counter(aa).most_common()[0][0])
consensus_seq = "".join(consensus_list)

ref_seq = None
highest_score = 0
for record in alignment:
    # globalxx finds the best global alignment between both sequences.
    # The first x indicates that identical characters are given 1 point, 0 otherwise
    # Second x indicates no gap penalties
    pairwise_score = pairwise2.align.globalxx(record.seq,
                                              consensus_seq,
                                              score_only=True
                                              )
    if pairwise_score > highest_score:
        highest_score = pairwise_score
        ref_seq = record
    elif pairwise_score == highest_score:
        # If they are equal, you can prioritize other things
        pass

You can again obtain the consensus sequence, and then calculate the Levenshtein (edit) distance between each record and the consensus. The sequence with the lowest distance will be the most representative. The easiest way to calculate it is via installing the python-Levenshtein module with pip install python-Levenshtein.

from Levenshtein import distance as edit_distance

# First, calculate the consensus as described above

ref_seq = None
best_distance = 999999999999
for record in alignment:
    distance = edit_distance(record.seq, consensus_seq)
    if distance < best_distance:
        best_distance = distance
        ref_seq = record
    elif distance == best_distance:
        # If they are equal, you can prioritize other things
        pass

This is a very good use of pairwise2. Levenshtein distance in any other walk of life is cool, wrt to genetic distance would have its critiques.

I loved your approach it put a smile on my face. Thank you @albertr

Suggestions in increasing order of relevance:

The only program that is close is rootdigger.
This finds the sequence most likely to be the root, as the name suggests. Available via GitHub, but not via conda.

You could just write your PIM approach as small program? It wouldn't be difficult.

Alternatively, you could just select the longest, shortest or median branch length and use that as a representative sequence. This wouldn't be computationally expensive at all (you'd need the correct root however and a rooted tree*).

The shortest branch length from the root is a reasonable solution. You can do it via a GUI (if its e.g. a Bayesian tree, there's one program that does this), write you own code, or else manually via optical analysis (easy to spot the shortest branch in a rooted tree).

The shortest branch-length is the closest sequence to the ancestral sequence for the in-groups (non-root). You can obtain a reconstruction of the ancestral sequence - it is computationally (very) expensive and isn't a real sequence.
*, Point 3 and 4 require a phylogram rather than a dendrogram of course.


text:
drawing multiple sequence alignment as tree in R
I am very new here. Please help me with my codes as am trying to draw multiple sequence alignment as tree suing ggtree package in R and i have following error.
library(tidyverse)
library(ggtree)
library(seqinr)
tree = read.tree("get_result.nwk")
msaplot(p=ggtree(tree), fasta = "get_results", window=c(150, 175))

Error in inds:inde : NA/NaN argument
> msaplot(p=ggtree(tree) + geom_tiplab(align=TRUE), fasta = "get_result.fasta", offset = 5)
Error in inds:inde : NA/NaN argument

My data looks like
LN681202.1_Ralstonia_solanacearum_isolate RsBe2
TGCAAGTCGAACGGCAGCGGGGGTAGCTTGCTACCTGCCGGCGAGTGGCGAACGGGTGAGTAATACATCGGAACGTGCCCTGTAGTGGGGGATAACTAGTCGAAAGACTAGCTAATACCGCATACGACCTGAGGGTGAAAGTGGGGGACCGCAAGGCCTCATGCTATAGGAGCGGCCGATGTCTGATTAGCTAGTTGGTGGGGTAAAGGCCCACCAAGGCGACGATCAGTAGCTGGTCTGAGAGGACGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATTTTGGACAATGGGGGCAACCCTGATCCAGCAATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTTGTCCGGAAAGAAATCGCTTCGGTTAATACCTGGAGTGGATGACGGTACCGGAAGAATAAGGACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTCCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTGTGCAAGACCGATGTGAAATCCCCGGGCTTAACCTGGGAATTGCATTGGTGACTGCACAGCTAGAGTGTGTCAGAGGGAGGTAGAATTCCACGTGTAGCAGTGAAATGCGTAGAGATGTGGAGGAATACCGATGGCGAAGGCAGCCTCCTGGGATAACACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCCTAAACGATGTCAACTAGTTGTTGGGGATTCATTTCCTTAGTAACGTAGCTAACGCGTGAAGTTGA
MF179616.1_Kv-1
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTCCGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MN725749.1_SA006
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTCCGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MF183971.1_Klebsiella_sp._strain_Kv-2
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTAGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MT507217.1_Uyi23r
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTAGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MF183969.1_Kv-3
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTAGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MK929064.1_NM43TS4-1
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTAGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MF183973.1_Kv-4
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTAGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MN082125.1_IILSFPR619
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTAGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MF183974.1_Kv-5
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTAGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MK671348.1_1
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTAGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MF183970.1_Kv-6
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTCGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MF682957.1_BT3409
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTCGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MF183972.1_Kv-7
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTCGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MZ475068.1_Klebsiella_variicola_EMBL-1
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTCGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
MF373424.1_KV8
GCTCTCGGGTGACGAGCGGCGGACGGGTGAGTAATGTCTGGGAAACTGCCTGATGGAGGGGGATAACCTGGAAACGGTAGCTAATACCGCATAACGTCGCAAGACCAAAGTGGGGGACCTTCGGCCTCATGCCATCAGATGTGCCCAGATGGGATTAGCTGGTAGGTGGGGTAACGGCTCCCTAGGCGACGATCCCTAGCTGGTCTGAGAGGATGACCAGCCAACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAAATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGACTTTCAGCGGGGAGGAAGGCGGTGAGGTTAATAACCTCATCGATTGACGTTACCCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCGTCAAGTCGGATGTGAAATCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGATAACCGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATG
KC853296.1_JM16
GCTCTCGGGTGACGAGCGGCGGACGGGTGAGTAATGTCTGGGAAACTGCCTGATGGAGGGGGATAACCTGGAAACGGTAGCTAATACCGCATAACGTCGCAAGACCAAAGTGGGGGACCTTCGGCCTCATGCCATCAGATGTGCCCAGATGGGATTAGCTGGTAGGTGGGGTAACGGCTCCCTAGGCGACGATCCCTAGCTGGTCTGAGAGGATGACCAGCCAACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAAATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGACTTTCAGCGGGGAGGAAGGCGGTGAGGTTAATAACCTTATCGATTGACGTTACCCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCGTCAAGTCGGATGTGAAATCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGATAACCGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATG

The tree info looks like this
(((((MF183974.1_Kv-5:0.0000000001,
(MN082125.1_IILSFPR619:0.0000000001,
(MF183973.1_Kv-4:0.0000000001,
(MK929064.1_NM43TS4-1:0.0000000001,
(MF183969.1_Kv-3:0.0000000001,
(MT507217.1_Uyi23r:0.0000000001,
MF183971.1_Klebsiella_sp._strain_Kv-2:0.0000000001)0.0000000000:0.0000000001)
0.0000000000:0.0000000001)0.0000000000:0.0000000001)0.0000000000:0.0000000001)
0.0000000000:0.0000000001)0.0000000000:0.0000000001,
MK671348.1_1:0.0000000001)0.8360000000:0.0012113715,
(MZ475068.1_Klebsiella_variicola_EMBL-1:0.0000000001,
(MF183972.1_Kv-7:0.0000000001,
(MF682957.1_BT3409:0.0000000001,
MF183970.1_Kv-6:0.0000000001)
0.0000000000:0.0000000001)0.0000000000:0.0000000001)0.0000000000:0.0000000001)
0.0000000000:0.0012061821,
((MN725749.1_SA006:0.0000000001,
MF179616.1_Kv-1:0.0000000001)
0.0000000000:0.0000024449,
LN681202.1_Ralstonia_solanacearum_isolate_RsBe2:99.9997264606)
0.0000000000:0.0000002190)1.0000000000:4.9050549661,
KC853296.1_JM16:0.0012459932,
MF373424.1_KV8:0.0000028057);

Firstly, do check tree is not an empty object, i.e. it uploads the tree file. Secondly, check the path to the alignment is correct get_results. Normally an alignment file would be suffixed with the alignment format, e.g. get_results.fa.
The immediate thing that strikes me, if all the paths are correct and the file names is the alignment format supplied isn't fasta format.
If you replace the contents of the file get_results (not get_results.nwk - because that's your tree file, with the following (wait ... see Note and Note2 ) ...
    >LN681202.1_Ralstonia_solanacearum_isolate_RsBe2
TGCAAGTCGAACGGCAGCGGGGGTAGCTTGCTACCTGCCGGCGAGTGGCGAACGGGTGAGTAATACATCGGAACGTGCCCTGTAGTGGGGGATAACTAGTCGAAAGACTAGCTAATACCGCATACGACCTGAGGGTGAAAGTGGGGGACCGCAAGGCCTCATGCTATAGGAGCGGCCGATGTCTGATTAGCTAGTTGGTGGGGTAAAGGCCCACCAAGGCGACGATCAGTAGCTGGTCTGAGAGGACGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATTTTGGACAATGGGGGCAACCCTGATCCAGCAATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTTGTCCGGAAAGAAATCGCTTCGGTTAATACCTGGAGTGGATGACGGTACCGGAAGAATAAGGACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTCCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTGTGCAAGACCGATGTGAAATCCCCGGGCTTAACCTGGGAATTGCATTGGTGACTGCACAGCTAGAGTGTGTCAGAGGGAGGTAGAATTCCACGTGTAGCAGTGAAATGCGTAGAGATGTGGAGGAATACCGATGGCGAAGGCAGCCTCCTGGGATAACACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCCTAAACGATGTCAACTAGTTGTTGGGGATTCATTTCCTTAGTAACGTAGCTAACGCGTGAAGTTGA
>MF179616.1_Kv-1
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTCCGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
>MN725749.1_SA006
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTCCGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
>MF183971.1_Klebsiella_sp._strain_Kv-2
 GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTAGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC
>MT507217.1_Uyi23r
GGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCTGTAAACGATGTCGATTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTACGTTAAATCGACCGCCTGGGGAGTACGGCCGCAAGGAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGAACGCGAAGAACCTTACCTGGTCTTGACATCCACAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGGTTGTGAAATGTTGGGTTAAGTCCCGCAACCGCAACCCTTATCCTTTGTTGCCAGCGGTTAGGCCGGGAACTCAAAGGAGACTGCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACCCAGGGCTACACACGTGCTCAATGGCATATACAAAGAGAAGCGACCAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTCGGTGAATACGTTCCCGGGCCTTGTACACACCGC

Also ....

Please remove ALL space after the taxa name both in the alignment file and the treefile (I've done it for you in the alignment file)
LN681202.1_Ralstonia_solanacearum_isolate RsBe2 - the space is a problem between the word isolate and RsBe2 ... please make sure in the treefile the space is replaced with a _ character.

The names in the treefile must exactly match the names in the alignment file. Just to confirm, the last alignment file you forwarded had more taxa (that space is still an issue BTW), however ...

Fasta format must start the taxa name with a > otherwise it will not recognise it.

I think thats the basic error and why its saying null because it can't parse the alignment file. Getting the treefile names precisely aligned with the alignment taxa names might be an issue.

Note
My fasta alignment format still looks like a dogs dinner, I can't get the fasta format on the markdown language working ....

fasta format there must be a carriage return between the taxa name and the sequence.
the > must not have any spaces between the start of the line (unlike my version) until the > ... nor any spaces anywhere on the line ...
Spaces within the taxa name could cause problems in Newick tree format (depends what program you've used).

Note2
The alignment I edited and the new alignment you just supplied (presumably complete) obviously can't be simply cut and paste as I originally thought, but you get the gist. Writing code is a much easier way to do phylogenetics because there's very little manual editing and parsers do everything for you.

In the tree file you've correctly named LN681202.1_Ralstonia_solanacearum_isolate_RsBe2, but the same name must in the alignment file. Generally taxa names don't use spaces and are replaced with _, in phylip format for example this causes mayhem with the parser.

Finally
If thats all thats wrong it would be cool, I think they'll be more to get it working correctly.

yes thats what went wrong.. it resolved the issue.. thank you all for your response and help..

Thanks @ManasaK.G. for the feedback, glad its resolved. Could you kindly mark the question 'resolved' if this was the solution please?

A separate question would be cool, if I can help I will. @ManasaK.G. .


text:
Expression analysis of miRNAs with normal RNA-seq data without small RNA-seq data
I am looking to perform expression analysis of miRNAs with normal RNA-seq data lacking small RNA-seq data?
Which path should I choose for known miRNAs and unknown new miRNAs?
Data set: rna-seq data that analyzes the expression differences of a plant under stress.
Objective To assess known and new miRNAs.

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.

This looks a good question, outside my area. It may be helpful to provide further details of your study and data set and clarify what the known miRNAs represent.

There is rna-seq data that analyzes the expression differences of a plant under stress. From this data I would like to learn the expression profiles of known and new miRNAs.

You are going to have to look up the library prep protocol used, and determine if it will capture miRNA.  My guess is, in the course of most RNASeq preps, small products are removed as contamination, so you won't have any real miRNA left behind.
I believe you need to use a special library prep designed for miRNA to study them.


text:
How can to validate the presence of a certain type of cells in a single cell dataset?
I have a single cell dataset which I consider to be a reference , let say, for an human organ, I have identified some new clusters that correspond to cell types. I also have WGCNA modules that correspond to a identified cluster.
Given this background, is there any possibilities to validate the presence of the cell types from my reference dataset inside another single cell dataset ?
I was thinking about GSEA, but all the gene in a set have the same importance.
Could I use eigen vectors from first principal component for a given module expressed in the corresponding cell type, as a weight. Then calculate inside another dataset a sort of "first principal component" using the previous eigen vectors. Then I would simply calculate the correlation between the first principal component and the calculated one to validate whether my cell type is present or not.
I was also thinking about a sort of weigthed GSEA, if anything like this ever existed.

Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.

I think the typical way to do this would be to identify a small number of marker genes whose presence and absence is specific for that cell type.


text:
How to convert fasta alignment to nexus or phylip format?
I generated a multiple sequence alignment using mafft, but I would like to convert it to nexus or phylip format (required by a downstream software). What's the easiest way to do the conversion?

EMBO's bioinformatic suite EMBOSS has seqret program that can do exactly that. You can install it via conda (conda install -c bioconda emboss) and then the conversion to nexus is
seqret -sequence input.mafft.fasta -outseq output.mafft.nex -osformat nexus

from Bio import AlignIO
from pathlib import Path
from Bio.AlignIO.PhylipIO import SequentialPhylipWriter
import re

file = 'input.mafft.fasta'
fileoutnex = re.sub('fasta', 'nex', file)
pathway = '/mypath/'
alignment = AlignIO.read(Path(pathway, file), 'fasta')

# write out in nexus
AlignIO.write(alignment, Path(pathway, fileoutnex), 'nexus')

# write out in relaxed phylip
fileoutphy = re.sub('fasta', 'phy', file)
with open(Path(pathway, fileoutphy), 'w') as output_handle:
    SequentialPhylipWriter(output_handle).write_alignment(alignment, id_width=30)

The advantage here is you can select what phylip format you want. That is a BIG advantage, traditional phylip format is terrible, particularly with taxa ids lengths. Here relaxed phylip format is being used.
The other advantages,

Easily parse, change, alter the taxa names;
Phylip taxa id can be ANY length (id_width=);
save it as a script;
no need to write an outfile (ok minor);
pipe the alignment straight into downstream stuff;
very easy manipulate the alignment.


text:
The difference between sequence-independent and sequence-order independent structural alignment?
I am confused about the difference between sequence-independent and sequence-order independent structural alignment. TM-align for example claims to be sequence-independent; however, I've seen it is also sequence order independent from the papers. To me, they sound like the same thing, but obviously, they aren't. Does anyone know about anything the difference?

Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.


text:
Find SNPs in yeast genomes
I'm a new Bioinformatic scientist working for a yeast genetics company.
Objective To create a database of yeast genomes from NCBI and identify SNP variants.
In my pipeline

FastQC,
Trimmomatic,
BWA
GATK

The method being to check the quality of the downloaded reads, trim off any unnecessary information, and to align them prior to VCF.
NCBI sequence data comes from both Illumina sequencing and PacBio.
Questions

Is the pipeline reasonable, for example, is BWA ok over more recent developments?
Could the same pipeline be used both for Illumina and PacBio sequences?

Note
I don't plan on assembling any of the reads I have as I'm just looking to comb through to find variations.

I attempted to edit the question with a view to it being more focused, albeit remains 'broad'. The OP can decide whether this is okay. In case the OP is unaware the preferred format is a focused, specific question to invite an equally focused answer. Further focusing the question to provide a single question would be great. The other components can be asked in separate question(s).

I'd suggest using minimap2 for long reads, BWA is not well suited to higher error reads. See the settings under its -x flag for the specific technology.

Thank you @M__ for cleaning up my question it reads a lot better now.

Would you reccomend BWA for Illumina reads and then minimap2 for PacBio reads then? @MaximilianPress

I'd recommend using minimap2 for both. I believe that Heng Li has written that minimap2 is at least as good as bwa for Illumina if you set the -x sr flag.

For pacbio variant calling, there are a few resources online: https://www.biostars.org/p/393823/, https://gatk.broadinstitute.org/hc/en-us/community/posts/360072716972-Variant-calling-with-PacBio-HiFi-reads

Hi @rimo, please look at the answer and Maximillian's response and consider upvoting and/or accepting.

for illumina reads (only)
prerequisite: download n prepare

the fna n gff file of yeast reference

mapping_in_02.directory

1st step is mapping the downloaded reads to the reference genome:

build index for the reference using the following command

bwa index ../01./GCF_000146045.2_R64_genomic.fna

index the reference genome for rapid searching and aligning

do the mapping using bwa mem (better with bash shell script, then there will be no need for each individual one by one)

After mapping, you could use GATK4 for the variation calling based on the mapping result.

The steps of
variation calling by GATK4 include dealing with the mapping result ( mark duplicates and add read group ), raw variation
identification ( joint call followed by single individual variation calling ), quality recalibration and realign, second round of variation
calling and final variation filtering ( filtering is
applied to haplotyping to remove uninformative mutations. An obvious filter is to remove SNPs with identical calls in all the samples ).
The final variation calling result with SNPs and InDels could be obtained and the final vcf file could be sent to SnpEff for further annotation ( an easier way is using SnpEff in Galaxy with the right reference genome version,  eg  R64-1-1.75 ).
The isolates from NCBI would indicate that there are 16 chromosomes and one mitochondrial chromosome after SnpEff analysis.  In the analysis, the numbers of insertions and deletions will be obtained.

domesticated populations of Far East Asia-->  https://www.nature.com/articles/s41467-018-05106-7

method within this 2021 manuscript--> https://www.frontiersin.org/articles/10.3389/fmicb.2021.631250/full

Chinese explanation:  https://www.jianshu.com/p/9b5aac4b43d9  (if your company could find someone to explain this recent web site.)


text:
difficult in runing the R code for ggtree
I tried to run the below code but ended having issue. I have checked thoroughly  and I didn't find any mismatch in fasta sequence
tree = read.tree("phylotree.txt")

ggtree(tree, layout='circular') +
   geom_tiplab(offset=4, align=TRUE) + xlim(NA, 12)
msaplot(tree, fasta ="phylotree.fasta", window=c(120, 200))

Error in msaplot(tree, fasta = "phylotree.fasta", window = c(120,
200)) :    taxa name in input sequences are not match with the ones on
the tree, please check your input files...