BOL: Related items

Patterns: a modeling tool dedicated to biological network modeling

Abhimanyu Singh — Fri, 26 Jul 2019 01:11:59 -0500

It is designed to work with patterned data. Famous examples of problems related to patterned data are:

recovering signals in networks after a stimulation (cascade network reverse engineering),
analysing periodic signals.

Address of the bookmark: https://github.com/fbertran/Patterns

Run bash script in Perl program !

Neel — Sat, 27 Feb 2021 01:42:23 -0600

BioPerl is a compilation of Perl modules that can be used to build bioinformatics-related Perl scripts. It is used, for example, in the development of source codes, standalone software/tools, and algorithms in bioinformatics programmes. Different modules are easy to instal and include, making it easier to perform different functions. Despite the fact that Python is commonly favoured over Perl, some bioinformatics software, such as the standalone version of 'alienomics', is written in Perl. Often it's a major problem for beginners to execute certain Unix/shell commands in Perl script, so it's hard to determine which feature is unique to a situation.

Perl provides various features and operators for the execution of external commands (described as follows), which are unique in their own way.

They vary slightly from one another, making it difficult for Perl beginners to choose between them.

1. IPC::Open2

More at https://bioinformaticsonline.com/snippets/view/42919/perl-ipcopen2-module

2. exec””

syntax: exec "command";

It's a Perl function (perlfunc) that executes a command but never returns, similar to a function's return statement.

While running the command, it keeps processing the script and does not wait until it finishes first, returns false when the command is not found, but never returns true.

3. Backticks “ or qx//

syntax: `command`;

syntax: qx/command/;

It's a Perl operator (perlop) that executes a command and then resumes the Perl script once the command has ended, but the return value is the command's STDOUT.

4. IPC::Open3

syntax: $output = open3(\*CHLD_IN, \*CHLD_OUT, \*CHLD_ERR, 'command arg1 arg2', 'optarg',...);

It is very similar to IPC::Open2 with the capability to capture all three file handles of the process, i.e., STDIN, STDOUT, and STDERR. It can also be used with or without the shell. You can read about it more in the documentation: IPC::Open3.

$output = open3(my $o ut, my $in, 'command arg1 arg2');

OR without using the shell

$output = open3(my $out, my $in, 'command', 'arg1', 'arg2');

5.a2p

syntax: a2p [options] [awkscript]

There is a Perl utility known as a2p which translates awk command to Perl. It takes awk script as input and generates a comparable Perl script as the output. Suppose, you want to execute the following awk statement

awk '{$1 = ""; $2 = ""; print}' f1.txt

This statement gives error sometimes even after escaping the variables (\$1, \$2) but by using a2p it can be easily converted to Perl script:

For further information, you can read it’s documentation: a2p

More help at https://bioinformaticsonline.com/snippets/view/42920/perl-script-to-run-awk-inside-perl

6. system()

syntax: system( "command" );

It is also a Perl function (perlfunc) that executes a command and waits for it to get finished first and then resume the Perl script. The return value is the exit status of the command. It can be called in two ways:

system( "command arg1 arg2" );

system( "command", "arg1", "arg2" );

HELP

Here are some useful Perl cheat sheets that can be used as a quick reference guide.-- https://www.pcwdld.com/perl-cheat-sheet

Bioinformatics JRF/SRF position at NII

Sun, 25 May 2014 16:54:04 -0500

NATIONAL INSTITUTE OF IMMUNOLOGY, NEW DELHI-110067

Applications are invited for the position of Senior Research Fellow for the following time-bound sponsored project as per the details given below:

1. BTIS project on, “Bioinformatics Center-National Infrastructural Facility in the Area of Immunology” funded by DBT

Senior Research Fellow (P) (One Position only)

Dr. Debasisa Mohanty
Staff Scientist-VI
deb@nii.res.in

Qualifications: M.Sc in Biological Sciences or Biotechnology with at least 04 years of Research experience in Bioinformatics or computational Biology after the master’s degree is essential.

Emoluments: The selected candidates will draw consolidated emoluments as per Institute Rules, depending upon qualifications & experience

Rs. 18,000/- per month consolidated plus 30% HRA if Leading to Ph.D/NET/GATE Qualified otherwise Rs. 14,000/- per month + 30% HRA.

Job description: The candidate should be well versed in programming in PERL/C++/HTML/CGI, web server and portal development, computational analysis of
protein structure & function, molecular dynamics simulations and use of high performance computing systems.

GENERAL TERMS AND CONDITIONS:-

1. The candidates selected for the above posts will be on contract for one year or duration of the project whichever is shorter, at a time.
2. No hostel/ housing facility will be provided.
3. Number of posts may vary and shall be need based. Advertisement is no commitment.
4. Applicants may clearly mention the category they belong to i.e. SC/ST/OBC/PH and attach documentary proof of the same.
5. No TA/DA will be paid for attending the interview, if called for.
6. Apart from sending application in the prescribed format given below, candidates should send complete Curriculum Vitae along with the names of three referees. Curriculum Vitae should contain details of the experimental expertise.

HOW TO APPLY Interested candidates may apply directly, STRICTLY IN THE PRESCRIBED FORMAT GIVEN BELOW, through e-mail, to the Investigator of the project, clearly indicating the name of the project along with their complete C.V., e-mail id, fax numbers, telephone numbers. Only Short listed candidates will be called for interview and they required to submit attested copies of all their certificates and a Demand Draft of Rs 100/- drawn on Canara Bank or Indian Bank payable at Delhi/New Delhi in favour of the Director, NII (SC / ST and PH candidates are exempted subject to submission of documentary proof), at the time of interview.

LAST DATE OF RECEIPT OF APPLICATIONS: 06th June, 2014

www1.nii.res.in/sites/default/files/projectappointment-Dr.Mohanty-6June2014.pdf

MUM&Co is a simple bash script that uses Whole Genome Alignment information provided by MUMmer (v4) to detect variants.

Rahul Nayak — Wed, 27 Apr 2022 04:34:12 -0500

MUM&Co is able to detect:
Deletions, insertions, tandem duplications and tandem contractions (>=50bp & <=150kb)
Inversions (>=1kb) and translocations (>=10kb)

Address of the bookmark: https://github.com/SAMtoBAM/MUMandCo

Bioinformatics JRF vacancy at ICGEB, New Delhi

Wed, 23 Jul 2014 16:07:15 -0500

Junior Research Fellow for a DBT sponsored project entitled "Computational and experimental characterization of stage specific arginine methylation in P. falciparum proteome".

Candidates should have a 1st class MSc/MTech/BTech degree in Bioinformatics. Please send complete CV, quoting Application for RMETH-JRF-2014, by email to Dr. Dinesh Gupta: dinesh@icgeb.res.in

Closing date for applications: 6 August 2014

More at http://www.icgeb.org/tl_files/Vacancies/JRF.pdf

d3Network:Tools for creating D3 JavaScript network, tree, dendrogram, and Sankey graphs from R.

Jit — Fri, 06 Apr 2018 12:10:45 -0500

Mike Bostock’s D3.js is great for creating interactive network graphs with JavaScript. The d3Network package makes it easy to create these network graphs from R. The main idea is that you should able to take an R data frame with information about the relationships between members of a network and create full network graphs with one command.

Address of the bookmark: http://christophergandrud.github.io/d3Network/

Linux Sort Commands for Bioinformatics

Rahul Nayak — Sat, 31 May 2014 15:41:16 -0500

Almost all the scripting languages such as Perl, Python etc have built-in sort, but unfortunately none of them are as flexible as sort command. But one when it come to space efficiency GNU sort stands at the top. It can sort a 20Gb file with less than 2Gb memory. It is not trivial to implement so powerful a sort by yourself.

sort a space-delimited file based on its first column, then the second if the first is the same, and so on:
sort input.txt

sort a huge file (GNU sort ONLY):
sort -S 1500M -t $HOME/tmp input.txt > sorted.txt

sort starting from the third column, skipping the first two columns:
sort +2 input.txt

sort the second column as numbers, descending order; if identical, sort the 3rd as strings, ascending order:
sort -k2,2nr -k3,3 input.txt

sort starting from the 4th character at column 2, as numbers:
sort -k2.4n input.txt

More Linxu sort command information

If you have any sort commands you'd like to share, please add them to our comments section below. For more help, you can also type:

man sort

or

sort --help

on your Unix/Linux system.

Tools for Protein-Protein Docking !

Poonam Mahapatra — Wed, 25 Apr 2018 05:15:53 -0500

Predicting the structure of protein–protein complexes using docking approaches is a difficult problem whose major challenges include identifying correct solutions, and properly dealing with molecular flexibility and conformational changes. Following are the tools to predict the structure of protein–protein complexes:

3D-Dock Suite

Global rigid search: FFTShape complementarity and electrostatics

Re-scoring and clustering. Refinement of interface side-chains

3D-Garden

Global rigid search in ensamble

Shape complementarity and Lennard–Jones potential

Side chain and backbone dihedral refinement

DOT

Global rigid search: FFTShape complementarity, electrostatics and VDWNone

Escher NG

Global rigid searchShape complementarity, hydrogen bonds and electrostatic

Integrated in VEGA

GRAMM

Global rigid search: FFT. smooth protein surface representation for soft docking

Shape complementarity and Lennard-Jones potential

Clustering of conformations

GRAMM-X

Global rigid search: FFT. smooth protein surface representation for soft docking

Shape complementarity and Lennard-Jones potentialminimization and re-scoring with multiple filters

HEX

Global rigid search: Fourier correlation of spherical harmonics

Shape complementarity

HADDOCK

Global rigid searchElectrostatic ,VDW and desolvation energy termsMD simulated annealing refinement . Filtering based on external data.

ICM

Global rigid search: Monte CarloEmpirical scoring function

Clustering and selection of conformations. Refinement of interface side-chains and re-scoring

MolFit

Global rigid search: FFTShape complementarity

Clustering of good solutions, filtering using a priori information and small, local rigid rotations around selected conformations

PatchDock

Global rigid searchShape complementarity and atomic desolvation energy

Clustering of conformations

PyDock

Global rigid search:FFTShape complementarity

rescoring by binding electrostatics and desolvation energy

RosettaDock

Local rigid search: Monte Carlo with low and high resolution structure representation levels

Different scoring parameters for the different resolutions

ZDOCK

Global rigid search: FFTShape complementarity, desolvation energy, and electrostatics.

Energy minimization and re-scoringFree for academics

Point to note:

The proper treatment of flexibility in protein–protein docking is still an active field of research. You first should analyzed your proteins in order to define their conformational space and then choose the most suitable method for your docking problem.

Next generation sequencing in R or bioconductor environment

John Parker — Mon, 02 Jun 2014 18:03:09 -0500

There are many R software and bioconductor packages for NGS data analysis, some of them are as follows

Biostrings

The Biostrings package from Bioconductor provides an advanced environment for efficient sequence management and analysis in R. It contains many speed and memory effective string containers, string matching algorithms, and other utilities, for fast manipulation of large sets of biological sequences. The objects and functions provided by Biostrings form the basis for many other sequence analysis packages. Documentation

IRanges Overview

IRanges provides the low-level infrastructure and containers for handling sets of integer ranges within Bioconductor's BioC-Seq domain. Its classes and methods provide support for many more high-level packages like GenomicRanges, ShortRead, Rsamtools, etc. Documentation

GenomicRanges Overview

The GenomicRanges package serves as the foundation for representing genomic locations within the Bioconductor project. It is built upon the IRanges infrastructure and defines three major data containers - GRanges, GRangesList and GappedAlignments - which are supporting other important BioC-Seq packages including ShortRead, Rsamtools, rtracklayer, GenomicFeatures and BSgenome. Compared to the IRanges container, the GRanges/GRangesList classes are more flexible and extensible to store additional information about sequence ranges, such as chromosome identifiers (sequence space), strand information and annotation data. Documentation

Motif Discovery

cosmo

The cosmo package allows to search a set of unaligned DNA sequences for a shared motif that may function as transcription factor binding site. The algorithm extends the popular motif discovery tool MEME (Bailey and Elkan, 1995) in that it allows the search to be supervised by specifying a set of constraints that the motif to be discovered must satisfy. Documentation

BCRANK

BCRANK is a method that takes a ranked list of genomic regions as input and outputs short DNA sequences that are overrepresented in some part of the list. The algorithm was developed for detecting transcription factor (TF) binding sites in a large number of enriched regions from high-throughput ChIP-chip or ChIP-seq experiments, but it can be applied to any ranked list of DNA sequences. Documentation

rGADEM: Documentation

MotIV: Documentation

ShortRead

The ShortRead package provides input, quality control, filtering, parsing, and manipulation functionality for short read sequences produced by high throughput sequencing technologies. While support is provided for many sequencing technologies, this package is primairly focused on Solexa/Illumina reads. Documentation

Rsamtools

Rsamtools provides functions for parsing and inspecting samtools BAM formatted binary alignment data. SAM/BAM is quickly becoming a universal standard alignment format, and is now supported by a wide variety of alignment tools. Documentation

Samtools Website
BWA (Burrows-Wheeler Alignment) Website

Additional tools for SNP analysis:

snpMatrix

BSgenome

BSgenome provides an object oriented infrastructure for interacting with a Biostring based genome sequence. BSgenome packages exist for many common genomes, and can be created to represent custom genomes. See the "How to forge a BSgenome data package" Vignette for instructions to create a new BSgenome package if a prebuilt package does not exist for your organism. Documentation

rtracklayer

rtracklayer provides an interface for exporting annotation feature data to various genome browsers and file formats (such as GFF). See the Small RNA Profiling exercise for an example of using rtracklayer to visualize alignment coverage. Documentation

biomaRt

The biomaRt package, provides an interface to a growing collection of databases implementing the BioMart software suite (http:// www.biomart.org). The package enables online retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas. This data is retrieved automatically via the Internet, so it's recommended that you cache the data locally, or check versions if your code will be adversely affected by updates to these data. Documentation

ChIP-Seq Analysis Packages

Bioconductor provides various packages for analyzing and visualizing ChIP-Seq data. Only a small selection of these packages is introduced here. Additional useful introductions to this topic are: BioC ChIP-seq Case Study and BioC ChIP-Seq.

chipseq

The chipseq package combines a variety of HT-Seq packages to a pipeline for ChIP-Seq data analysis. Documentation

BayesPeak

BayesPeak is a peak calling package for identifying DNA binding sites of proteins in ChIP-Seq experiments. Its algorithm uses hidden Markov models (HMM) and Bayesian statistical methods. The following sample code introduces the identification of peaks with the BayesPeak package as well as the incorporation of read coverage information obtained by the chipseq package. Documentation [ Publication ]

PICS

The PICS package applies probabilistic inference to aligned-read ChIP-Seq data in order to identify regions bound by transcription factors. PICS identifies enriched regions by modeling local concentrations of directional reads, and uses DNA fragment length prior information to discriminate closely adjacent binding events via a Bayesian hierarchical t-mixture model. The following sample code uses the test data set from the above BayesPeak package in order to compare the results from both methods by identifying their consensus peak set. Documentation [ Publication ]

ChIPpeakAnno

The ChIPpeakAnno package provides. batch annotation of the peaks identified from either ChIP-seq or ChIP-chip experiments. It includes functions to retrieve the sequences around peaks, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. The package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. Documentation

Additional ChIP-Seq Packages

DiffBind: Documentation

MOSAICS: Documentation

iSeq: Documentation

ChIPseqR: Documentation

ChiPsim: Documentation

CSAR: Documentation

ChIP-Seq Pipeline: PICS, rGADEM and MotIV (developer web site)

SPP: ChIP-seq processing pipeline

SPP Tutorial

MACS

SIPeS

RNA-Seq Analysis

Counting Reads that Overlap with Annotation Ranges

The GenomicRanges package provides support for importing into R short read alignment data in BAM format (via Rsamtools) and associating them with genomic feature ranges, such as exons or genes. This way one can quantify the number of reads aligning to annotated genomic regions. The package defines general purpose containers for storing genomic intervals as well as more specialized containers for storing alignments against a reference genome. The two main functions for read counting provided by this infrastructure are countOverlaps and summarizeOverlaps. For their proper usage, it is important to read the corresponding PDF manual. Documentation

Differential Gene Expression Analysis with DESeq

The DESeq package contains functions to call differentially expressed genes (DEGs) in count tables based on a model using the negative binomial distribution. It expects as input a data frame with the raw read counts per region/gene of interest (rows) for each test sample (columns). Such a count table can be imported into R or generated from BAM alignment files using the countOverlaps function as introduced above. Documentation

Differential Gene Expression Analysis with edgeR

The edgeR package uses empirical Bayes estimation and exact tests based on the negative binomial distribution to call differentially expressed genes (DEGs) in count data.

Documentation

A variety of additional R packages are available for normalizing RNA-Seq read count data and identifying differentially expressed genes (DEG):

easyRNASeq (simplifies read counting per genome feature)

DEXSeq (Inference of differential exon usage); parathyroidSE explains how to generate exon read counts in R

DEGseq

baySeq (also see: segmentSeq)

Genominator (Bullard et al. 2010)

Detection of Alternative Splice Junctions

Another utility of RNA-Seq experiments is the analysis of splice junctions. The following software suggestions provide this utility:

ERANGE
TopHat

SpliceMap

SplitSeek

DNA-Methylation Data Analysis

methylPipe
bsseq
BiSeq
Much more under BiocViews

HT-Seq Data Visualization

ggbio: ggplot2 extension for genomics data (online manual) Gviz: Plotting data and annotation information along genomic coordinates HilbertVis: Hilbert genome plots

GenomeGraphs: Plotting genomic information from Ensembl

TileQC: Flow Cell Quality Visualization

rtracklayer: R interface to genome browsers

genoPlotR: Plotting maps of genes and genomes

Genominator: Tools for storing, accessing, analyzing and visualizing genomic data.

To install all packages

source("http://bioconductor.org/biocLite.R")
biocLite()
biocLite(c("ShortRead", "Biostrings", "IRanges", "BSgenome", "rtracklayer", "biomaRt", "chipseq", "ChIPpeakAnno", "Rsamtools", "BayesPeak", "PICS", "GenomicRanges", "DESeq", "edgeR", "leeBamViews", "GenomicFeatures", "BSgenome.Celegans.UCSC.ce2"))

EvidentialGene: tr2aacds, mRNA Transcript Assembly Software

Rahul Nayak — Tue, 08 May 2018 04:39:39 -0500

EvidentialGene is a genome informatics project, "Evidence Directed Gene Construction for Eukaryotes", to construct high quality, accurate gene sets for animals and plants, developed by Don Gilbert at Indiana University, see
http://arthropods.eugenes.org/EvidentialGene/

Construction refers to the combination of classical gene prediction, and more recent gene assembly (de-novo and genome-assisted) methods. The basic Evigene methods involve using available best-of-breed gene prediction and assembly software, combining all evidence for genes, from expressed sequences, genome assembly sequences, related species protein sequences, and any other, to annotate and score gene constructions. Over-produced constructions are classified by gene evidence for best qualities per "locus", including genome-aligned and gene-transcript aligned (genome-free) locus identification. All software developed for EvidentialGene is publicly available. See project wiki/blog for notes.

Download

http://arthropods.eugenes.org/EvidentialGene/trassembly.html

https://sourceforge.net/p/evidentialgene/blog/

Address of the bookmark: http://arthropods.eugenes.org/EvidentialGene/trassembly.html