BOL: Related items

Linux command line exercises for NGS data processing

Jit — Wed, 22 Jun 2016 07:59:39 -0500

The purpose of this tutorial is to introduce students to the frequently used tools for NGS analysis as well as giving experience in writing one-liners. Copy the required files to your current directory, change directory (cd) to the linuxTutorial folder, and do all the processing inside:

[uzi@quince-srv2 ~/]$ cp -r /home/opt/MScBioinformatics/linuxTutorial .
[uzi@quince-srv2 ~/]$ cd linuxTutorial
[uzi@quince-srv2 ~/linuxTutorial]$

I have deliberately chosen Awk in the exercises as it is a language in itself and is used more often to manipulate NGS data as compared to the other command line tools such as grep, sed, perl etc. Furthermore, having a command on awk will make it easier to understand advanced tutorials such as Illumina Amplicons Processing Workflow.

In Linux, we use a shell that is a program that takes your commands from the keyboard and gives them to the operating system. Most Linux systems utilize Bourne Again SHell (bash), but there are several additional shell programs on a typical Linux system such as ksh, tcsh, and zsh. To see which shell you are using, type

[uzi@quince-srv2 ~/linuxTutorial]$ echo $SHELL

/bin/bash

Address of the bookmark: http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/linux.html

Bioinformatics tools and software

Jit — Tue, 05 Jul 2016 10:02:26 -0500

USEARCH >
Extreme high-throughput sequence analysis. Orders of magnitude faster than BLAST. MUSCLE >
Multiple sequence alignment. Faster and more accurate than CLUSTALW.

UPARSE >
OTU clustering for 16S and other marker genes. Highly accurate OTU sequences and improved diversity measures. UCHIME >
Chimeric sequence detection. PILER >
De novo genome repeat finder. PILER-CR >
Detection of CRISPR repeats in bacterial genomes. QSCORE >
Compare two multiple alignments for benchmarking. PALS >
Whole-genome alignment. PREFAB >
Protein Reference Alignment Database. MSA benchmark collection >
Selected multiple alignment benchmarks in a standardized FASTA format.

Address of the bookmark: http://drive5.com/software.html

MEGAN6

Neel — Mon, 25 Jul 2016 05:45:22 -0500

Microbiome analysis using a single application

MEGAN6 is a comprehensive toolbox for interactively analyzing microbiome data. All the interactive tools you need in one application.

Taxonomic analysis using the NCBI taxonomy or a customized taxonomy such as SILVA
Functional analysis using InterPro2GO, SEED, eggNOG or KEGG
Bar charts, word clouds, Voronoi tree maps and many other charts
PCoA, clustering and networks
Supports metadata
MEGAN parses many different types of input

Why use MEGAN6?

The software is:

Easy to use. MEGAN6 is a single application and all features are available through menus, toolbars and graphics. No scripting skills required.
Powerful. MEGAN6 allows you to work with hundreds of samples containing hundreds of millions of sequencing reads. Blast-like analysis can be performed using DIAMOND.
Comprehensive. MEGAN6 offers a large range of analysis tools, and is under active development.

Address of the bookmark: https://ab.inf.uni-tuebingen.de/software/megan6

GeneBreak: a tool to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach

Jit — Sat, 01 Oct 2016 15:15:29 -0500

Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R (www.cran.r-project.org) and is available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).

Address of the bookmark: http://www.bioconductor.org/packages/release/bioc/html/GeneBreak.html

EXCAVATOR2tool

Bulbul — Wed, 30 Nov 2016 04:09:19 -0600

EXCAVATOR2 is a collection of bash, R and Fortran scripts and codes that analyses Whole Exome Sequencing (WES) data to identify CNVs. EXCAVATOR2 enhances the identification of all genomic CNVs, both overlapping and non-overlapping targeted exons by integrating the analysis of In-targets and Off- targets reads. Specifically, it improves the precision of calling CNVs overlapping targeted exons from WES data and enlarges the spectrum of detectable CNVs to off-target events.
EXCAVATOR2 can be effectively employed for the identification of CNVs in small as well as large-scale re-sequencing population and cancer studies. Lastly, it’s of particular interest that all WES experiments can be re-analysed using our method with the beneficial effect to identify novelCNVs in extra-exonic regions by having the full-genome CN profile.

Address of the bookmark: https://sourceforge.net/projects/excavator2tool/

Maq: Mapping and Assembly with Quality

Jit — Tue, 22 Nov 2016 04:51:39 -0600

Maq stands for Mapping and Assembly with Quality It builds assembly by mapping short reads to reference sequences. Maq is a project hosted by SourceForge.net. The project page is available athttp://sourceforge.net/projects/maq/. Maq is previously known as mapass2.

Run Maq Now

Follow these steps to try Maq. All you need is a reference sequence file in the FASTA format.

Prepare a reference sequence (ref.fasta). Better a bacterial genome.
Download maq, maq-data and maqview at the download page.
Copy maq, maq.pl and maq_eval.pl to the $PATH or to the same directory.
Simulate diploid reference and read sequences, map reads, call variants and evaluate the results in one go:
```
maq.pl demo ref.fasta calib-30.dat
```
where calib-30.dat is contained in maq-data.

View the alignment:

cd maqdemo/easyrun;
maqindex -i -c consensus.cns all.map;
maqview -c consensus.cns all.map

Even for advanced maq users, running `maq.pl demo' is recommended. You may find something helpful.

Address of the bookmark: http://maq.sourceforge.net

fqtools

Jit — Thu, 08 Dec 2016 09:31:12 -0600

fqtools is a software suite for fast processing of FASTQ files. Various file manipulations are supported. See below for a full list of the subcommands available and a brief description of their purpose. Most of the individual subcommands will take either a single file or a pair of files as input. If no input file is specified, fqtools will attempt to read data from stdin. In this case, it is advisabe to specify the format of the data provided. For subcommands that generate FASTQ data, either a single file or a pair of files will be generated. If no -o argument is provided, single files will be writted to stdout.

Address of the bookmark: https://github.com/alastair-droop/fqtools

Understanding Greedy Algorithms

Jit — Mon, 12 Dec 2016 04:37:40 -0600

Learning greedy algo for biologist.

https://www.topcoder.com/community/data-science/data-science-tutorials/greedy-is-good/

This webpage is also useful for the same:

http://learninglover.com/examples.php?id=59

http://www.cs.rpi.edu/~magdon/ps/conference/super_biokdd.pdf

https://ocw.mit.edu/courses/biology/7-91j-foundations-of-computational-and-systems-biology-spring-2014/lecture-slides/MIT7_91JS14_Lecture6.pdf

http://schatzlab.cshl.edu/teaching/AssemblyClass/01.%20Assembly%20Intro.pdf

http://lsl.sinica.edu.tw/Services/Class/files/20150612449.pdf

http://www.cs.jhu.edu/~langmea/resources/lecture_notes/assembly_scs.pdf

https://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-43.pdf

Address of the bookmark: https://www.topcoder.com/community/data-science/data-science-tutorials/greedy-is-good/

Harvest

Jit — Tue, 31 Jan 2017 10:57:56 -0600

Harvest is a suite of core-genome alignment and visualization tools for quickly analyzing thousands of intraspecific microbial genomes, including variant calls, recombination detection, and phylogenetic trees.

Tools

Parsnp - Core-genome alignment and analysis
Gingr - Interactive visualization of alignments, trees and variants
HarvestTools - Archiving and postprocessing

Citation

Treangen TJ, Ondov BD, Koren S, Phillippy AM. The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes. Genome Biology, 15 (11), 1-15 [PDF]

Address of the bookmark: http://harvest.readthedocs.io/en/latest/index.html

HivePlot

Jit — Thu, 16 Feb 2017 11:39:34 -0600

The hive plot is a rational visualization method for drawing networks. Nodes are mapped to and positioned on radially distributed linear axes — this mapping is based on network structural properties. Edges are drawn as curved links. Simple and interpretable.

The purpose of the hive plot is to establish a new baseline for visualization of large networks — a method that is both general and tunable and useful as a starting point in visually exploring network structure.

More at http://www.hiveplot.com/

Address of the bookmark: http://www.hiveplot.com/