Python Programming for Biology: Bioinformatics and Beyond


High-throughput sequence analyses



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[Tim J. Stevens, Wayne Boucher] Python Programming

17

High-throughput sequence analyses

Contents

High-throughput sequencing

Sequencing for biochemical analysis

Short-read mapping

Python examples

Mapping sequences to a genome

Obtaining genome sequences

Indexing a genome

Aligning reads to a genome

Using the HTSeq library

Reading sequences from FASTQ files

Reading a genome alignment file

Matching sequence reads to genome data

High-throughput sequencing

Given the decreasing cost required to determine the sequence of nucleic acids, sequencing

is used in increasingly wider contexts. Rather than only determining the genome sequence

of  an  organism,  high-throughput  techniques  allow  researchers  to  investigate  much  more,

such  as  the  variation  within  individuals  of  a  population,  the  amount  of  expression  of

individual genes in a given sample (e.g. by detecting RNAs) and the sequences which are

bound to particular protein components. A sequencing run on one of the latest-generation

sequencing machines may generate many gigabases (>10

9

bp) of data and so much of the



task for bioinformatics is to make sense of the raw sequence data: to put it into a genomic,

biological context. For organisms with a known genomic sequence the primary task when

processing  high-throughput  sequence  data  is  to  simply  map  relatively  short  bits  of

sequence  called  ‘reads’  that  come  from  the  sequencing  machine  to  a  reference  genome.

Only  then  can  the  detected  sequences  be  understood.  By  mapping  newly  acquired

sequences on to the known chromosomes the whole database of information that annotates

the genome, such as the position of genes and regulatory sequences, indicates which DNA

features  were  detected.  In  this  chapter  we  will  give  an  introduction  to  various  basic

computational  procedures  involving  high-throughput  sequence  data  which  can  be

achieved, or at least handled, using Python. Because this is a vast and rapidly expanding

subject  we  can  only  lightly  touch  on  the  core  concepts  here,  though  hopefully  we  have



provided solid starting points for further development.


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