BOL: Related items

karyoploteR: plot whole genomes with arbitrary data

Abhimanyu Singh — Fri, 02 Feb 2018 03:24:28 -0600

karyoploteR is an R package to create karyoplots, that is, representations of whole genomes with arbitrary data plotted on them. It is inspired by the R base graphics system and does not depend on other graphics packages. The aim of karyoploteR is to offer the user an easy way to plot data along the genome to get broad genome-wide view to facilitate the identification of genome wide relations and distributions.

Address of the bookmark: https://bernatgel.github.io/karyoploter_tutorial/

QuIN’s web server

Jit — Mon, 27 Jun 2016 10:44:16 -0500

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions.

AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub:https://github.com/UcarLab/QuIN/.

Address of the bookmark: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004809

Fancy Oneliner for Bioinformatics !!

Poonam Mahapatra — Thu, 07 Jul 2016 12:05:50 -0500

This webpage lists some of the one-liners that we frequently use in metagenomic analyses. You can click on the following links to browse through different topics. You can copy/paste the commands as they are in your terminal screen, provided you follow the same naming conventions and folder structures as we have. We are sharing these codes with the intention that if they are useful and help you in your analyses, then we will be appropriately credited as considerable effort has been put into devising them.

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

BBMap help

Shruti Paniwala — Mon, 10 Oct 2016 06:29:03 -0500

BBMAP • a solution for everything

That content has been reformatted and it is being expanded to include more information.

There are common options for most BBMap suite programs and depending on the file extension the input/output format is automatically chosen/set.

Using BBMap

Mapping Nanopore reads

BBMap.sh has a length cap of 6kbp. Reads longer than this will be broken into 6kbp pieces and mapped independently.

More at https://www.biostarhandbook.com/tools/bbmap/bbmap-help.html

Address of the bookmark: https://www.biostarhandbook.com/tools/bbmap/bbmap-help.html

Statistics Using R with Biological Examples

Neel — Thu, 03 Nov 2016 04:55:41 -0500

This book is a manifestation of my desire to teach researchers in biology a bit more about statistics than an ordinary introductory course covers and to introduce the utilization of R as a tool for analyzing their data. My goal is to reach those with little or no training in higher level statistics so that they can do more of their own data analysis, communicate more with statisticians, and appreciate the great potential statistics has to offer as a tool to answer biological questions.

This is necessary in light of the increasing use of higher level statistics in biomedical research. I hope it accomplishes this mission and encourage its free distribution and use as a course text or supplement.

K Seefeld, May 2007

Professor (all levels) in Bioinformatics and Computational Biology

Tue, 22 Nov 2016 05:43:38 -0600

King Abdullah University of Science and Technology (KAUST) (kaust.edu.sa) is seeking a highly motivated and skilled faculty member for the Bioinformatics track whose research focuses on development of methods and tools for Bioinformatics and Computational Biology.
KAUST is an international, graduate-level research university dedicated to advancing science and technology through interdisciplinary research, education, and innovation. Located on the shores of the Red Sea in Saudi Arabia, KAUST offers superb research facilities, generous assured research funding, and internationally competitive salaries, attracting top international faculty, scientists, engineers, and students to conduct fundamental and goal-oriented research to address the world’s pressing scientific and technological challenges in the areas of food, water, energy, and the environment.
The successful applicant is expected to develop world-leading research in domain of bioinformatics/computational biology with focus on development of novel computational approaches for efficient and accurate methods of analyzing biological phenomena at molecular level. The faculty member will be part of the Computational Bioscience Research Center (CBRC) within the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division. The position will remain open until filled.

Requirements:

PhD or equivalent in a Computer Science, Mathematics or Engineering discipline. Candidates should be well-established within the research field relevant to the position grade. They should demonstrate original research and experience at the highest international level.

Responsibilities and tasks:

Research competence in the following areas is preferred:
Analysis of next generation sequencing (NGS) and other ‘omics’ data (e.g. CAGE, ChIP-Seq, DHS, RNA-Seq, Ribo-Seq, proteomic, metabolic and NMR spectra, etc.).
Signaling, regulatory and metabolic pathways analysis.
Development of tools (web-based and standalone) suited for efficient computational biology/bioinformatics.

Visit cemse.kaust.edu.sa to apply.

PRISM

Jit — Sat, 10 Dec 2016 15:19:40 -0600

PRISM is a software for split read (reads which span across a structrual variant -- SV ) mapping and SV calling from the mapping result. PRISM is able to detect small insertions and abitrary size deletions, inversions and tandom duplications with the direction of discordant read pairs. PRISM_CTX is a tool for detecting inter-chromosome trans-location events.

PRISM and PRISM_CTX were originally designed and written by Michael Brudno and Yue Jiang, The original PRISM publication can be found here.

The authors may be contacted via e-mail at: prism at cs.toronto.edu.

Additional information is available in the PRISM README file and PRISM_CTX README file.

http://compbio.cs.toronto.edu/prism/

Address of the bookmark: http://compbio.cs.toronto.edu/prism/

HTSlib

Jit — Wed, 15 Mar 2017 11:38:05 -0500

Samtools is a suite of programs for interacting with high-throughput sequencing data. It consists of three separate repositories:

Samtools: Reading/writing/editing/indexing/viewing SAM/BAM/CRAM format
BCFtools: Reading/writing BCF2/VCF/gVCF files and calling/filtering/summarising SNP and short indel sequence variants
HTSlib: A C library for reading/writing high-throughput sequencing data

Samtools and BCFtools both use HTSlib internally, but these source packages contain their own copies of htslib so they can be built independently.

Address of the bookmark: http://www.htslib.org/

Fastq format

Jit — Wed, 03 May 2017 04:23:32 -0500

FASTQ format is a text-based format for storing both a biological sequence (usually nucleotide sequence) and its corresponding quality scores. Both the sequence letter and quality score are each encoded with a single ASCII character for brevity.

It was originally developed at the Wellcome Trust Sanger Institute to bundle a FASTA sequence and its quality data, but has recently become the de facto standard for storing the output of high-throughput sequencing instruments such as the Illumina Genome Analyzer.^[1]

Address of the bookmark: https://en.wikipedia.org/wiki/FASTQ_format

Quick next generation sequencing (NGS) terms definition

Neel — Fri, 09 Jun 2017 04:52:26 -0500

fragment size: the Illumina WGS protocol generates paired-end reads from both ends of longer fragments. The lengths of these fragments are assumed to be sampled from a normal distribution. Therefore, in the absence of structural variants, mapping locations of the paired ends span within an interval [δmin,δmax]. Most (>90%) of paired-end reads are sampled from no-SV regions, therefore the fragment size distribution can be learned empirically for each WGS data set separately.

concordant reads: a read pair is called concordant if they can be mapped to the reference genome as “expected”: (a) mapped to opposing strands where the upstream read is mapped to the forward strand and the downstream read is mapped to the reverse strand2, (b) the distance between ends is between the minimum and maximum expected fragment size.

discordant reads: briefly, any non-concordant read pair is considered discordant. Note that, by definition, the discordant read pairs signal potential SVs. The sequence signature produced by these type of reads is known as read-pair signature.

split reads: a read that can only be mapped to the reference genome by breaking into two sub-reads is called a split-read. These types of reads also indicate a potential SV or a short insertion or deletion (indel).

read depth: number of reads that map within a region of the genome. Overall genome-wide read depth is also referred to as depth of coverage. It is expected that the number of reads that “cover” each base-pair to follow a Poisson distribution. Therefore, if the read depth over a certain region deviates significantly from this distribution, it signals for a potential copy number variation (CNV).