BOL: Related items

Genetic code - Amino Acid

Poonam Mahapatra — Sun, 26 Oct 2014 07:45:58 -0500

The genetic code consists of 64 triplets of nucleotides. These triplets are called codons.With three exceptions, each codon encodes for one of the 20 amino acids used in the synthesis of proteins. That produces some redundancy in the code: most of the amino acids being encoded by more than one codon.

The image summarise all in one.

More at http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/C/Codons.html

Rosalind Bioinformatics problems !!!

Abhi — Thu, 18 Dec 2014 10:32:48 -0600

Rosalind is a platform for learning bioinformatics and programming through problem solving. Take a tour to get the hang of how Rosalind works.

http://rosalind.info/problems/list-view/

Address of the bookmark: http://rosalind.info/problems/list-view/

seqloc 0.6

Gudiya Pal — Sun, 28 Dec 2014 12:51:29 -0600

The Bio.SeqLoc modules in seqloc are designed to represent positions and locations (ranges of positions) on sequences, particularly nucleotide sequences. My original motivation for writing these packages was handing the locations of genes in eukaryotic genomes.

Handle sequence locations for bioinformatics http://www.ingolia-lab.org/seqloc-tutorial.html

Address of the bookmark: http://www.stackage.org/snapshot/nightly-2014-12-28/package/seqloc-0.6

Scripted DNA !!!

Jit — Mon, 17 Aug 2015 17:44:04 -0500

As per bioinformatician DNA is partially scripted ;) You dont believe in it. Please have a look at image carefully:)

Ensembl comparative genomics resources

Jitendra Narayan — Sun, 28 Feb 2016 17:10:20 -0600

The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available.

Database URL: http://www.ensembl.org.

Address of the bookmark: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4761110/

RATT

Jitendra Narayan — Sun, 07 Feb 2016 16:09:40 -0600

RATT is software to transfer annotation from a reference (annotated) genome to an unannotated query genome.

It was first developed to transfer annotations between different genome assembly versions. However, it can also transfer annotations between strains and even different species, like Plasmodium chabaudi onto P. berghei, between different Leishmania species or Salmonella enterica onto other Salmonella serotypes. RATT is able to transfer any entries present on a reference sequence, such as the systematic id or an annotator's notes; such information would be lost in a de novo annotation.

More at http://ratt.sourceforge.net/

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

Efficient genome searching with Biostrings and the BSgenome data package

Aasha — Mon, 07 Mar 2016 05:18:06 -0600

Address of the bookmark: https://www.bioconductor.org/packages/3.3/bioc/vignettes/BSgenome/inst/doc/GenomeSearching.pdf

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

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

Genome STRiP

Neel — Tue, 06 Sep 2016 03:58:19 -0500

Genome STRiP (Genome STRucture In Populations) is a suite of tools for discovering and genotyping structural variations using sequencing data. The methods are designed to detect shared variation using data from multiple individuals.

Genome STRiP looks both across and within a set of sequenced genomes to detect variation. The methods are adaptive and support heterogeneous data sets, including variations in sequencing depth, read lengths and mixtures of paired and single-end reads. A minimum of 20 to 30 genomes are required to get acceptable results, but the method gains power across genomes and processing more genomes provide better results.

To run discovery or genotyping on a single sequenced genome or a small set of genomes, you need to call your data against a background population, such as a set of genomes from the 1000 Genomes Project. The background population does not need to be matched to the target individuals.

Address of the bookmark: http://software.broadinstitute.org/software/genomestrip/