def get_sample_info(fin):
	"""
	Open tab-delimited txt file containing the following columns:
	v0: customer_ID		| ID given to sample by customer
	v1: gigpad_ID		| A sample ID that may differ from "Customer_ID" and that is associated with library prior to pooling
	v2: lane			| Lane of sequencer where this sample's pool was run, needed to locate file output by HiSeq
	v3: index			| Six digit sequence of the index for this library
	v4: ercc_mix		| Mix of ERCC spike used for library construction (options: "1", "2", "-")
	v5: file_directory	| Directory where sample's files were written to by Casava
	v6: batch			| GIGPAD batch number associated with sample
	v7: label			| Biological condition associated with the sample, provided by customer
	v8: ref_genome		| Rerence genome associated with sample. (options: "hg19", "Zv9", "mm10")
	v9: library_type	| Type of library for sample (options: "PE", "SE", "DGE", "SPE",
							corresponding to: "paired-end", "single-end", "digital gene expression", "stranded paired-end")
	"""
	f = open(fin,'r')
	c = f.read().split('\n')[1:]
	if '' in c:
		c.remove('')
	d = []
	for x in c:
		customer_id = x.split('\t')[0]
		gigpad_id = x.split('\t')[1]
		lane = x.split('\t')[2]
		index = x.split('\t')[3]
		ercc_mix = x.split('\t')[4]
		top_dir = x.split('\t')[5]
		if top_dir[-1] != "/":
			top_dir = top_dir+"/"
		batch = x.split('\t')[6]
		label = x.split('\t')[7]
		ref_genome = x.split('\t')[8]
		library_type = x.split('\t')[9]
		d.append([customer_id, gigpad_id, lane, index, ercc_mix, top_dir, batch, label, ref_genome, library_type])
	return d