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Bioinformatics with Python Cookbook
Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook: Learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology , Second Edition

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Bioinformatics with Python Cookbook

Next-Generation Sequencing

In this chapter, we will cover the following recipes:

  • Accessing GenBank and moving around NCBI databases
  • Performing basic sequence analysis
  • Working with modern sequence formats
  • Working with alignment data
  • Analyzing data in VCF
  • Studying genome accessibility and filtering SNP data
  • Processing NGS data with HTSeq

Introduction

Next-generation sequencing (NGS) is one of the fundamental technological developments of the decade in life sciences. Whole genome sequencing (WGS), RAD-Seq, RNA-Seq, Chip-Seq, and several other technologies are routinely used to investigate important biological problems. These are also called high-throughput sequencing technologies, and with good reason: they generate vast amounts of data that needs to be processed. NGS is the main reason that computational biology has become a big-data discipline. More than anything else, this is a field that requires strong bioinformatics techniques.

Here, we will not discuss each individual NGS technique per se (this would require a whole book on its own). We will use an existing WGS dataset and the 1,000 Genomes Project to illustrate the most common steps necessary to analyze genomic data. The recipes presented here will be easily...

Accessing GenBank and moving around NCBI databases

Although you may have your own data to analyze, you will probably need existing genomic datasets. Here, we will look at how to access such databases at the National Center for Biotechnology Information (NCBI). We will not only discuss GenBank, but also other databases at NCBI. Many people refer (wrongly) to the whole set of NCBI databases as GenBank, but NCBI includes the nucleotide database and many others, for example, PubMed.

As sequencing analysis is a long subject, and this book targets intermediate to advanced users, we will not be very exhaustive with a topic that is, at its core, not very complicated. Nonetheless, it's a good warm-up for the more complex recipes that we will see at the end of this chapter.

Getting ready...

Performing basic sequence analysis

We will now do some basic analysis on DNA sequences. We will work with FASTA files and do some manipulation, such as reverse complementing or transcription. As with the previous recipe, we will use Biopython, which you installed in Chapter 1, Python and the Surrounding Software Ecology. These two recipes provide you with the necessary introductory building blocks with which we will perform all the modern NGS analysis and then genome processing in this and the next chapter, Chapter 3, Working with Genomes.

Getting ready

If you are using Jupyter Notebook, then open Chapter02/Basic_Sequence_Processing.ipynb. If not, you will need to download a FASTA sequence. We will use the human Lactase (LCT...

Working with modern sequence formats

Here, we will work with FASTQ files, the standard format output used by modern sequencers. You will learn how to work with quality scores per base and also consider the variations in output coming from different sequencing machines and databases. This is the first recipe that will use real data (big data) from the Human 1,000 Genomes Project. We will start with a brief description of the project.

Getting ready

The Human 1,000 Genomes Project aims to catalog worldwide human genetic variation and takes advantage of modern sequencing technology to do WGS. This project makes all data publicly available, which includes output from sequencers, sequence alignments, and SNP calls, among many other...

Working with alignment data

After you receive your data from the sequencer, you will normally use a tool such as Burrows-Wheeler Aligner (bwa) to align your sequences to a reference genome. Most users will have a reference genome for their species. You can read more on reference genomes in the next chapter, Chapter 3, Working with Genomes.

The most common representation for aligned data is the sequence alignment map (SAM) format. Due to the massive size of most of these files, you will probably work with its compressed version (BAM). The compressed format is indexable for extremely fast random access (for example, to speedily find alignments to a certain part of a chromosome). Note that you will need to have an index for your BAM file, which is normally created by the tabix utility of SAMtools. SAMtools is probably the most widely-used tool for manipulating SAM/BAM files.

...

Analyzing data in VCF

After running a genotype caller (for example, GATK or SAMtools), you will have a VCF file reporting on genomic variations, such as SNPs, insertions/deletions (INDELs), copy number variations (CNVs), and so on. In this recipe, we will discuss VCF processing with the PyVCF module.

Getting ready

While NGS is all about big data, there is a limit to how much I can ask you to download as a dataset for this book. I believe that 2 to 20 GB of data for a tutorial is asking too much. While the 1,000 Genomes' VCF files with realistic annotations are in this order of magnitude, we will want to work with much less data here. Fortunately, the Bioinformatics community has developed tools to allow for the partial...

Studying genome accessibility and filtering SNP data

While the previous recipes were focused on giving an overview of Python libraries to deal with alignment and variant call data, in this recipe, we will concentrate on actually using them with a clear purpose in mind.

If you are using NGS data, chances are that your most important file to analyze is a VCF file, which is produced by a genotype caller such as SAMtools, mpileup, or GATK. The quality of your VCF calls may need to be assessed and filtered. Here, we will put in place a framework to filter SNP data. Rather than giving you filtering rules (an impossible task to be performed in a general way), we will give you procedures to assess the quality of your data. With this, you can devise your own filters. Be sure to check Chapter 11, Advanced NGS Processing for more tips on filtering.

...

Processing NGS data with HTSeq

HTSeq (https://htseq.readthedocs.io) is an alternative library that's used for processing NGS data. Most of the functionality made available by HTSeq is actually available in other libraries covered in this book, but you should be aware of it as an alternative way of processing NGS data. HTSeq supports, among others, FASTA, FASTQ, SAM (via pysam), VCF, GFF, and Browser Extensible Data (BED) file formats. It also includes a set of abstractions for processing (mapped) genomic data, encompassing concepts like genomic positions and intervals or alignments. A complete examination of the features of this library is beyond our scope, so we will concentrate on a small subset of features. We will take this opportunity to also introduce the BED file format.

The BED format allows for the specification of features for annotations tracks. It has many...

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Key benefits

  • Perform complex bioinformatics analysis using the most important Python libraries and applications
  • Implement next-generation sequencing, metagenomics, automating analysis, population genetics, and more
  • Explore various statistical and machine learning techniques for bioinformatics data analysis

Description

Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data. This book covers next-generation sequencing, genomics, metagenomics, population genetics, phylogenetics, and proteomics. You'll learn modern programming techniques to analyze large amounts of biological data. With the help of real-world examples, you'll convert, analyze, and visualize datasets using various Python tools and libraries. This book will help you get a better understanding of working with a Galaxy server, which is the most widely used bioinformatics web-based pipeline system. This updated edition also includes advanced next-generation sequencing filtering techniques. You'll also explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks such as Dask and Spark. By the end of this book, you'll be able to use and implement modern programming techniques and frameworks to deal with the ever-increasing deluge of bioinformatics data.

Who is this book for?

This book is for Data data Scientistsscientists, Bioinformatics bioinformatics analysts, researchers, and Python developers who want to address intermediate-to-advanced biological and bioinformatics problems using a recipe-based approach. Working knowledge of the Python programming language is expected.

What you will learn

  • Learn how to process large next-generation sequencing (NGS) datasets
  • Work with genomic dataset using the FASTQ, BAM, and VCF formats
  • Learn to perform sequence comparison and phylogenetic reconstruction
  • Perform complex analysis with protemics data
  • Use Python to interact with Galaxy servers
  • Use High-performance computing techniques with Dask and Spark
  • Visualize protein dataset interactions using Cytoscape
  • Use PCA and Decision Trees, two machine learning techniques, with biological datasets
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Table of Contents

11 Chapters
Python and the Surrounding Software Ecology Chevron down icon Chevron up icon
Next-Generation Sequencing Chevron down icon Chevron up icon
Working with Genomes Chevron down icon Chevron up icon
Population Genetics Chevron down icon Chevron up icon
Population Genetics Simulation Chevron down icon Chevron up icon
Phylogenetics Chevron down icon Chevron up icon
Using the Protein Data Bank Chevron down icon Chevron up icon
Bioinformatics Pipelines Chevron down icon Chevron up icon
Python for Big Genomics Datasets Chevron down icon Chevron up icon
Other Topics in Bioinformatics Chevron down icon Chevron up icon
Advanced NGS Processing Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
(4 Ratings)
5 star 50%
4 star 0%
3 star 25%
2 star 0%
1 star 25%
math_guy51 Jun 19, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I know of no other book covering the Advanced Bioinformatics/Biopython topics found in this book.This is not an introductory book ...
Amazon Verified review Amazon
Soup Isarangkoon Jul 10, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is very insightful with all the step by step instructions on different projects one can try with Bioinformatics using Python. This book is not too simplistic (some previous knowledge assumed), meanwhile it is not too hard that readers cannot follow through. If one wants to jump into Bioinformatics with Python, this is the book to read!Word of Warning: Some knowledge in Bioinformatics and Python is assumed in the book.
Amazon Verified review Amazon
RUser Feb 09, 2021
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Dieses Buch ist nicht für "Anfänger" geeignet. Was es nicht leistet: Es erklärt nicht die Sprache Python und nicht die im Buch verwendeten Algorithmen. Es ist ja auch eine Rezeptsammlung!!! Allerdings muss man die Küche vorher selbst bauen oder um es konkret zu sagen: Man sollte sich mit Anaconda auskennen - und das am besten auf Linux oder Ubuntu... Unter Windows wird man vermutlich nur den halben Spaß haben, da einige benötigte Tools dort nicht laufen. Der Autor erklärt das gut. Leider ist der Code insofern veraltet, als nicht mehr zugängliche Libraries verwendet werden. An einigen Stellen hilft dann Auskommentieren des Codes weiter. Der Autor hat auf Anfrage nach Hinweisen zur Korrektur des Codes auch bisher nur geantwortet, dass das Buch 2018 geschrieben sei und zu dem Zeitpunkt der Code noch funktionierte (die Abschaffung der problematischen Libraries wurde auch damals schon länger debattiert). Für ein Buch, zu dem eine Seite mit Druckfehlern existiert (die leer ist) und ein Repository im Internet (enthält aber nur die nicht rund laufenden Codes), ist das schwach. Ich hätte mir zumindest aktualisierten Code gewünscht. Immerhin merkt man dem Buch an, wie gerne der Autor seine Faszination weitergeben würde. Außerdem konnte ich ein Rezept nach einigen kleinen Korrekturen und Anpassungen auch für eine spielerische Analyse von COVID-DNA verwenden. Daher gebe ich gerne die drei Sterne als Bewertung. Aber ich sage es nochmal ausdrücklich: Um eine Sammlung von Rezepten, die man ohne Vorkenntnisse und Mühe nachkochen kann, handelt es sich nicht!
Amazon Verified review Amazon
Alejandro Marquez Apr 11, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
El libro llegó sin el empaquetado correcto (no emplayado como usualmente venden los libros), doblado de varias hojas y pasta, además las hojas del canto manchadas al parecer con lo que podrían ser dedos. Estamos en cuarentena por covid-19 y debería venir protegido el ejemplar lo mejor posible y contar con el emplayado habitual de los libros que es el que evita sea manoseado directamente.
Amazon Verified review Amazon
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