BOL: Related items

Understanding Fastqc Output

Jit — Fri, 15 Apr 2016 05:47:40 -0500

Understanding Following table and graphs

Duplication level
kmer profile
per base GC content
per base N content
per base quality
per base sequence content
per sequence GC content
per sequence quality
sequence length distribution

More at http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/

Address of the bookmark: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/

Bioinformatics Faculty at TNU

Wed, 27 Apr 2016 10:07:15 -0500

TNU is hiring faculty as Professors, Associate Professors and Assistant Professors in the following research and teaching programs.

Basic Sciences

Biotechnology & Bioinformatics

Qualifications are as per UGC norms. Please visit www.ugc.ac.in.

Educational, Age and experience requirements will be relaxed in exceptional cases.

Interested candidates are requested to apply with their complete CV with list of Publications, Patents, Industry and teaching experience at Undergraduate and Postgraduate level along with other relevant information to hr@tnu.in

Provide names and contact details of at least two references for confidential evaluation.

We follow Central University pay scales and few additional perks.

TNU follows highly liberal HR Policy with no salary constraint for exceptional candidates.

Applications are invited for the positions of - Professor, Associate Professor & Assistant Professor.
https://drive.google.com/file/d/0B0Y1Fpxmzyd3OHQ3aUNYa3hZY2c/view?usp=sharing

Reference
- Times of India, Ascent as on 20.04.2016.

YASS :: genomic similarity search tool

Jit — Mon, 02 May 2016 09:26:00 -0500

YASS is a genomic similarity search tool, for nucleic (DNA/RNA) sequences in fasta or plain text format (it produces local pairwise alignments). Like most of the heuristic pairwise local alignment tools for DNA sequences (FASTA, BLAST, PATTERNHUNTER, BLASTZ/LASTZ, LAST ...), YASS uses seeds to detect potential similarity regions, and then tries to extend them to local alignments. This genomic search tool uses multiple transition constrained spaced seeds that enable to search more fuzzy repeats, as non-coding DNA/RNA. Another simple, but interesting feature is that you can specify the seed pattern used in the search step (as provided for example by iedera).

Main features of YASS are:

multiple, possibly overlapping seeds and a new hit criterion to ensure a good sensitivity/selectivity trade-off
transition-constrained spaced seeds to improve sensitivity (transition mutations are purine to purine [A<->G] or pyrimidine to pyrimidine [C<->T])
using different scoring schemes with bit-score and E-value evaluated according to the sequence background frequencies
parameterizable output filter for low complexity repeats
reporting of various alignment statistical parameters (mutation bias along triplets, transition/transversion)
post-processing step to group gapped alignments

Address of the bookmark: http://bioinfo.lifl.fr/yass/

Lawley Lab

Mon, 09 May 2016 03:29:51 -0500

Lawley Lab are covered with a complex microbial community, known as our microbiota, which plays important roles in our physiology, immunity, metabolism and sustenance. Within the human gastrointestinal tract alone there are over 1,000 bacterial species, which amounts to approximately 10 times more cells than we harbor in our entire body and 200 times more genes than are found within our genome. Lawley Lab are really a 'supraorganism' consisting of our 'human' and 'microbial' selves.

More at http://www.sanger.ac.uk/science/groups/lawley-lab

cutadapt

Radha Agarkar — Fri, 13 May 2016 04:54:50 -0500

Cutadapt finds and removes adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.

Cleaning your data in this way is often required: Reads from small-RNA sequencing contain the 3’ sequencing adapter because the read is longer than the molecule that is sequenced. Amplicon reads start with a primer sequence. Poly-A tails are useful for pulling out RNA from your sample, but often you don’t want them to be in your reads.

Cutadapt helps with these trimming tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.

Cutadapt comes with an extensive suite of automated tests and is available under the terms of the MIT license.

If you use cutadapt, please cite DOI:10.14806/ej.17.1.200 .

Address of the bookmark: https://cutadapt.readthedocs.io/en/stable/installation.html#quickstart

BLOSUM50 Matrix

Radha Agarkar — Sat, 21 May 2016 22:12:15 -0500

BioGPS

Anjana — Mon, 23 May 2016 03:15:46 -0500

A free extensible and customizable gene annotation portal, a complete resource for learning about gene and protein function.

http://biogps.org/#goto=welcome

Address of the bookmark: http://biogps.org/#goto=welcome

ClueGO

Jit — Thu, 02 Jun 2016 09:51:44 -0500

ClueGO is a Cytoscape plug-in that visualizes the non-redundant biological terms for large clusters of genes in a functionally grouped network and it can be used in combination with GOlorize.

Address of the bookmark: http://www.ici.upmc.fr/cluego/

methylKit

Jit — Fri, 03 Jun 2016 10:09:29 -0500

methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods such as Agilent SureSelect methyl-seq. In addition, methylKit can deal with base-pair resolution data for 5hmC obtained from Tab-seq or oxBS-seq. It can also handle whole-genome bisulfite sequencing data if proper input format is provided.

Address of the bookmark: https://github.com/al2na/methylKit

4DGenome

Jitendra Narayan — Mon, 04 Jul 2016 00:44:55 -0500

Records in 4DGenome are compiled through comprehensive literature curation of experimentally-derived and computationally-predicted interactions. The current release contains 4,433,071 experimentally-derived and 3,605,176 computationally-predicted interactions in 5 organisms. Experimental data cover both high throughput datasets and individiual focused studies.

All interaction data are freely available in a standardized file format. Records can be queried by genomic regions, gene names, organism, and detection technology.

Address of the bookmark: http://4dgenome.research.chop.edu/