BOL: Related items

Katju Lab

Fri, 26 Feb 2016 03:25:32 -0600

TheLab seek to understand the genetic factors contributing to genomic variation and phenotypic diversity. To this end, we employ molecular and bioinformatic tools to study evolutionary processes at the level of populations, both experimental and natural, and genomes. Our research interests encompass a wide range of topics, including the evolution of organellar and nuclear genomes, gene duplication and the origin of novel function, and the fitness and phenotypic consequences of mutation in evolution. For details regards ongoing projects, please see the Research page.

http://katjulab.com/research.html

BUSCO

Jitendra Narayan — Sun, 07 Feb 2016 16:02:39 -0600

Assessing genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs

More at http://busco.ezlab.org/

Address of the bookmark: http://busco.ezlab.org/

Genome Browser : GBrowse

Jit — Fri, 19 Feb 2016 09:22:43 -0600

Generic Genome Browser Version 2: A Tutorial for Administrators

This is an extensive tutorial to take you through the main features and gotchas of configuring GBrowse as a server. This tutorial assumes that you have successfully set up Perl, GD, BioPerl and the other GBrowse dependencies. If you haven't, please see the GBrowse HOWTO During most of the tutorial, we will be using the "in-memory" GBrowse database (no relational database required!) Later we will show how to set up a genome size database using the berkeleydb and MySQL adaptors.

More at http://elp.ucdavis.edu/tutorial/tutorial.html

Address of the bookmark: http://elp.ucdavis.edu/tutorial/tutorial.html

RCircos: an R package for Circos 2D track plots

Jit — Fri, 20 May 2016 11:01:13 -0500

RCircos package provides a simple and flexible way to make Circos 2D track plots with R and could be easily integrated into other R data processing and graphic manipulation pipelines for presenting large-scale multi-sample genomic research data. It can also serve as a base tool to generate complex Circos images.

More at https://bitbucket.org/henryhzhang/rcircos/src

Address of the bookmark: https://bitbucket.org/henryhzhang/rcircos/src

SRF Bioinformatics job position in National Institute of Plant Genome Research (NIPGR)

Mon, 19 Sep 2016 05:43:38 -0500

SRF Bioinformatics job position in National Institute of Plant Genome Research (NIPGR)
Title : “Transcriptome and small RNA diversity analysis of developing seed contrasting rice varieties”
Qualification : Candidates having M.Sc./M.Tech. degree or equivalent (with minimum 60% marks) in Bioinformatics with a minimum of two years of post M.Sc./M.Tech research experience are eligible to apply.
No. of Post : 01
How to apply
Application should reach to Dr. Pinky Agarwal, Staff Scientist, National Institute of Plant Genome Research (NIPGR) Aruna Asaf Ali Marg, P.O. Box NO. 10531, New Delhi - 110067 on or before 30/09/2016

More at http://www.nipgr.res.in/careers/vacancies_latest.php#

Strudel

Anjana — Fri, 30 Sep 2016 09:47:02 -0500

Strudel is our graphical tool for visualizing genetic and physical maps of genomes for comparative purposes. The application aims to let the user examine their data at a variety of different levels of resolution, from entire maps to individual markers, and explore syntenic relationships between genomes. All browsing and interaction with Strudel happens in real-time – there is no need to wait while the maps are generated. It is built using Java 1.6 and ships with its own JRE, so there is no need for users to install or update Java.

Address of the bookmark: https://ics.hutton.ac.uk/strudel/

MIRO : miRNA omics

Jit — Tue, 04 Oct 2016 14:50:48 -0500

The MIRO (the miRNA omics) pipeline is a flexible and powerful tool for the analysis of miRNA (or more generall short RNA) expression using short-read deep sequencing data. In its present implementation MIRO is especially adapted for the analysis of reads generated with the Illumina sequencing platform. MIRO allows to preprocess the Solexa-reads, map them flexibly to several reference genomes using one of four different mappers, create differential gene (miRNA) expression profiles and cluster reads using one of several algorithm. MIRO output is furthermore compatible with software such as genome browsers and miRDeep.

Address of the bookmark: http://seq.crg.es/download/software/Miro/

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/

pyScaf

Bulbul — Mon, 19 Dec 2016 14:20:33 -0600

pyScaf orders contigs from genome assemblies utilising several types of information:

paired-end (PE) and/or mate-pair libraries (NGS-based mode)
long reads (NGS-based mode)
synteny to the genome of some related species (reference-based mode)

Scaffolding

In reference-based mode, pyScaf uses synteny to the genome of closely related species in order to order contigs and estimate distances between adjacent contigs.

Contigs are aligned globally (end-to-end) onto reference chromosomes, ignoring:

matches not satisfying cut-offs (--identity and --overlap)
suboptimal matches (only best match of each query to reference is kept)
and removing overlapping matches on reference.

In preliminary tests, pyScaf performed superbly on simulated heterozygous genomes based on C. parapsilosis (13 Mb; CANPA) and A. thaliana (119 Mb; ARATH) chromosomes, reconstructing correctly all chromosomes always for CANPA and nearly always for ARATH (Figures in dropbox, CANPA table, ARATH table).
Runs took ~0.5 min for CANPA on 4 CPUs and ~2 min for ARATH on 16 CPUs.

Important remarks:

Reduce your assembly before (fasta2homozygous.py) as any redundancy will likely break the synteny.
pyScaf works better with contigs than scaffolds, as scaffolds are often affected by mis-assemblies (no de novo assembler / scaffolder is perfect...), which breaks synteny.
pyScaf works very well if divergence between reference genome and assembled contigs is below 20% at nucleotide level.
pyScaf deals with large rearrangements ie. deletions, insertion, inversions, translocations. Note however, this is experimental implementation!
Consider closing gaps after scaffolding.

Address of the bookmark: https://github.com/lpryszcz/pyScaf

PANDASEQ

Shruti Paniwala — Mon, 23 Jan 2017 04:54:32 -0600

PANDASEQ assembles paired-end Illumina reads into sequences, trying to correct for errors and uncalled bases. The assembler reads two files in FASTQ format with quality information. If amplification primers were used (e.g., to isolate a variable region of the 16S gene, or the constant regions around zinc finger binding residues), they can be removed from the sequence during assembly. The final sequence will correct any uncalled bases in the overlapping region using the complementary strand. When mismatches occur in the overlapping region, the base with the better quality score is chosen.
The algorithm is as follows:

1.Find the positions where the forward and reverse primers match best above the threshold and discard the ends of the sequence, including the primer.
2.Pick and overlap to maximise the probability of the forward and reverse reads having come from a single piece of DNA.
3.Identify the masking of the end of the read with the quality score B or # as done by CASAVA and adjust the probabilities in this region.
4.Construct an assembled sequence between the primers and calculate the quality.
5.Check for various constraints, including quality, length, uncalled bases, and user-supplied modules.

http://neufeldserver.uwaterloo.ca/~apmasell/pandaseq_man1.html

Address of the bookmark: http://neufeldserver.uwaterloo.ca/~apmasell/pandaseq_man1.html