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Home > Programming > Application Development > Bioinformatics with Python Cookbook
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

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Bioinformatics with Python Cookbook

Chapter 2. 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 variant call format (VCF)
  • Studying genome accessibility and filtering SNP data

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 with good reason: they generate vast amounts of data that need to be processed. NGS is the main reason for computational biology becoming a "big data" discipline. More than anything else, this is a field that requires strong bioinformatics techniques. There is a very strong demand for professionals with these skillsets.

Here, we will not discuss each individual NGS technique per se (this will require a whole book on its own). We will use an existing WGS dataset and the human 1000 genomes project to illustrate the most common steps necessary to analyze genomic data. The recipes presented here will be easily applicable for other genomic...

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 see how to access these 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 more complex recipes at the end of this chapter.

Getting ready

We will use Biopython, which you installed in Chapter 1, Python and the Surrounding Software Ecology. Biopython provides an interface to Entrez, the data retrieval system made available by NCBI. This recipe is made available in the 01_NGS...

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 in which we will perform all the modern next-generation sequencing analysis, and then genome processing in this and the next chapter.

Getting ready

If you are using notebooks, then open 01_NGS/Basic_Sequence_Processing.ipynb. If not, you will need to download a FASTA sequence. We will use the human lactase gene as an example; you can get this using the knowledge you got from the previous recipe using Entrez research interface:

from Bio import Entrez, SeqIO
Entrez.email = "[email protected]"
hdl = Entrez.efetch(db='nucleotide', id=['NM_002299'], rettype='fasta&apos...

Working with modern sequence formats

Here, we will work with FASTQ files, the standard format output 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 1000 genomes project. We will start with a brief description of the project.

Getting ready

The human 1000 genomes project aims to catalog world-wide 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 artifacts. The name 1000 genomes is actually a misnomer because it currently includes more than 2500 samples. These samples are divided into 26 populations, spanning the whole planet. We will mostly use data from four populations: African Yorubans (YRI), Utah Residents with...

Working with alignment data

After you receive your data from the sequencer, you will normally use a tool such as 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.

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 normally created by the tabix utility of samtools. Samtools is probably the most widely used tool to manipulate SAM/BAM files.

Getting ready

As discussed in the previous recipe, we will use data from the 1000 genomes project. We will use the exome alignment for chromosome 20 of female NA18489. This is "just&quot...

Analyzing data in the variant call format

After running a genotype caller (for example, GATK or samtools), you will have a variant call format (VCF) file reporting on genomic variations, such as single-nucleotide polymorphisms (SNPs), Insertions/Deletions (INDELs), copy number variation (CNVs), and so on. In this recipe, we will discuss VCF processing with the PyVCF module.

Getting ready

While next-generation sequencing 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 1000 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 partial download of data. As part of the samtools/htslib package (http://www.htslib.org/), you can download tabix and bgzip, which will take care of data management (on Debian, Ubuntu, and...

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 with good reason: they generate vast amounts of data that need to be processed. NGS is the main reason for computational biology becoming a "big data" discipline. More than anything else, this is a field that requires strong bioinformatics techniques. There is a very strong demand for professionals with these skillsets.

Here, we will not discuss each individual NGS technique per se (this will require a whole book on its own). We will use an existing WGS dataset and the human 1000 genomes project to illustrate the most common steps necessary to analyze genomic data. The recipes presented here will be easily applicable for other genomic sequencing...

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Description

If you have intermediate-level knowledge of Python and are well aware of the main research and vocabulary in your bioinformatics topic of interest, this book will help you develop your knowledge further.

Who is this book for?

If you have intermediate-level knowledge of Python and are well aware of the main research and vocabulary in your bioinformatics topic of interest, this book will help you develop your knowledge further.

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Publication date : Jun 25, 2015
Length: 306 pages
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ISBN-13 : 9781783558650
Category :
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Concepts :
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Length: 306 pages
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Language : English
ISBN-13 : 9781783558650
Category :
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Languages :
Python
Concepts :
Application Development
Tools :
Biopython

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Table of Contents

10 Chapters
1. Python and the Surrounding Software Ecology Chevron down icon Chevron up icon
1. Python and the Surrounding Software Ecology
Introduction
Installing the required software with Anaconda
Installing the required software with Docker
Interfacing with R via rpy2
Performing R magic with IPython
2. Next-generation Sequencing Chevron down icon Chevron up icon
2. Next-generation Sequencing
Introduction
Accessing GenBank and moving around NCBI databases
Performing basic sequence analysis
Working with modern sequence formats
Working with alignment data
Analyzing data in the variant call format
Studying genome accessibility and filtering SNP data
3. Working with Genomes Chevron down icon Chevron up icon
3. Working with Genomes
Introduction
Working with high-quality reference genomes
Dealing with low-quality genome references
Traversing genome annotations
Extracting genes from a reference using annotations
Finding orthologues with the Ensembl REST API
Retrieving gene ontology information from Ensembl
4. Population Genetics Chevron down icon Chevron up icon
4. Population Genetics
Introduction
Managing datasets with PLINK
Introducing the Genepop format
Exploring a dataset with Bio.PopGen
Computing F-statistics
Performing Principal Components Analysis
Investigating population structure with Admixture
5. Population Genetics Simulation Chevron down icon Chevron up icon
5. Population Genetics Simulation
Introduction
Introducing forward-time simulations
Simulating selection
Simulating population structure using island and stepping-stone models
Modeling complex demographic scenarios
Simulating the coalescent with Biopython and fastsimcoal
6. Phylogenetics Chevron down icon Chevron up icon
6. Phylogenetics
Introduction
Preparing the Ebola dataset
Aligning genetic and genomic data
Comparing sequences
Reconstructing phylogenetic trees
Playing recursively with trees
Visualizing phylogenetic data
7. Using the Protein Data Bank Chevron down icon Chevron up icon
7. Using the Protein Data Bank
Introduction
Finding a protein in multiple databases
Introducing Bio.PDB
Extracting more information from a PDB file
Computing molecular distances on a PDB file
Performing geometric operations
Implementing a basic PDB parser
Animating with PyMol
Parsing mmCIF files using Biopython
8. Other Topics in Bioinformatics Chevron down icon Chevron up icon
8. Other Topics in Bioinformatics
Introduction
Accessing the Global Biodiversity Information Facility
Geo-referencing GBIF datasets
Accessing molecular-interaction databases with PSIQUIC
Plotting protein interactions with Cytoscape the hard way
9. Python for Big Genomics Datasets Chevron down icon Chevron up icon
9. Python for Big Genomics Datasets
Introduction
Setting the stage for high-performance computing
Designing a poor human concurrent executor
Performing parallel computing with IPython
Computing the median in a large dataset
Optimizing code with Cython and Numba
Programming with laziness
Thinking with generators
Index Chevron down icon Chevron up icon
Index

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Yannick Pouliot Oct 13, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I've been in computational biology for a long time, and I find this to be one of the most impressive practical and comprehensive books I've come across (and I go through a lot!) It provides an incredibly wide survey of different domains of computational biology (e.g., protein structure, genomics, and much more), combined with lots of good Python programming advice (e.g., use of IPython/Jupyter notebooks, provided by the author). And it's about as up to date as imaginable -- quite a tour de force!
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Stefanobia Dec 01, 2016
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Non sono in grado di recensire il prodotto in quanto non ho queste conoscenze, libro perfetto senza nessun segno o danneggiamento
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AR Nov 10, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
En mi caso, con poca experiencia en bioinformática, este libro me ha proporcionado una visión general de como se usa python en este campo.
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JoseJaime Jan 13, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Awesome, clear, updated. This is an must be readed textbook for bioinformaticians and biologist that want to tackle bioinformatics with Python.Python basis are mandatory.
Amazon Verified review Amazon
Loris Aug 29, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This book is an excellent bioinformatics cookbook and it covers a vast range of topics, ranging from next generation sequencing to population genetics and phylogenetics. However to take fully advantage of the book is highly recommended to have both a medium-high knowledge of python and some experience in bioinformatics. Each chapter provides python code to reproduce the analyses the author performs. Even though the author explains the code in details, the code often is not trivial and a certain experience with python is required. Similarly, a previous knowledge of bioinformatic technologies is desirable as the book often uses a terminology (such as fastq, bam, vcf, etc.) that won’t be familiar to those that haven’t any experience in bioinformatics.The book is well organized and structured. The first chapter helps the reader to set up a python environment through Anaconda or Docker. In the following chapter the author starts to talk about bioinformatics and the first topic he touches is NGS (next generation sequencing). I found this chapter very interesting as it shows how to deal efficiently with the usually huge fastq files and how to perform simple but useful analyses such as quality control of the sequencing reads by position. In the same chapter the author talks about the variant call format (VCF) and how to work with it through libraries such as pyVCF. The next chapter is dedicated to whole genome sequences in fasta format and to genome annotations in GFF/GTF formats. The following chapters are about population genetics and phylogenetics. Even though I’m not an expert of these fields and I was not aware of the formats used (such as genepop) I could enjoy the reading and the code was straightforward enough to be easily understandable. Then the author explains how to use PDB (the protein data bank) and the software PyMol. A very interesting (and in my opinion important) chapter is the last one where the author discusses how to deal efficiently with big genomics datasets. Here the authors talks about concurrency and ipython parallel computing, and shows how it is possible to speed up your python code by using libraries such as Cython and Numba.Overall the book is well written and organized and is a good cookbook for bioinformaticians.
Amazon Verified review Amazon

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About the authors

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Profile icon Tiago R Antao
Tiago R Antao
Tiago Antao is a bioinformatician currently working in the field of genomics. He was originally a computer scientist but he crossed over to computational biology with an MSc in Bioinformatics from the Faculty of Sciences at the University of Porto, Portugal, and a PhD on the spread of drug-resistant malaria from the Liverpool School of Tropical Medicine in the UK. He is one of the co-authors of Biopython, a major bioinformatics package written in Python. In his post-doctoral career, he has worked with human datasets at the University of Cambridge (UK) and with mosquito whole genome sequence data at the University of Oxford (UK). He is currently working as a research scientist at the University of Montana.
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Profile icon Tiago Antao
Tiago Antao
Github icon
Tiago Antao is a bioinformatician currently working in the field of genomics. A former computer scientist, Tiago moved into computational biology with an MSc in Bioinformatics from the Faculty of Sciences at the University of Porto (Portugal) and a PhD on the spread of drug-resistant malaria from the Liverpool School of Tropical Medicine (UK). Postdoctoral, Tiago has worked with human datasets at the University of Cambridge (UK) and with mosquito whole genome sequencing data at the University of Oxford (UK), before helping to set up the bioinformatics infrastructure at the University of Montana. He currently works as a data engineer in the biotechnology field in Boston, MA. He is one of the co-authors of Biopython, a major bioinformatics package written in Python.
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