BOL: Related items

Bioinformatics tools and software

Jit — Tue, 05 Jul 2016 10:02:26 -0500

USEARCH >
Extreme high-throughput sequence analysis. Orders of magnitude faster than BLAST. MUSCLE >
Multiple sequence alignment. Faster and more accurate than CLUSTALW.

UPARSE >
OTU clustering for 16S and other marker genes. Highly accurate OTU sequences and improved diversity measures. UCHIME >
Chimeric sequence detection. PILER >
De novo genome repeat finder. PILER-CR >
Detection of CRISPR repeats in bacterial genomes. QSCORE >
Compare two multiple alignments for benchmarking. PALS >
Whole-genome alignment. PREFAB >
Protein Reference Alignment Database. MSA benchmark collection >
Selected multiple alignment benchmarks in a standardized FASTA format.

Address of the bookmark: http://drive5.com/software.html

OrganellarGenomeDRAW

Jit — Tue, 16 Aug 2016 08:13:13 -0500

OrganellarGenomeDRAW is dedicated to convert genetic information stored in GenBank entries to graphical maps. The input text file has to be in GenBank flat file format, whereas the output format can be chosen among several formats. The application is especially optimized and adapted for the creation of high-quality, detailed circular maps of organellar genomes like the plastid genome (plastome) or the mitochondrial genome (chondriome). Nevertheless, you can upload any GenBank entry. The workflow is devided into three steps.

More at http://ogdraw.mpimp-golm.mpg.de/cgi-bin/ogdraw.pl

Address of the bookmark: http://ogdraw.mpimp-golm.mpg.de/index.shtml

TEannot

Jit — Thu, 18 Aug 2016 10:02:03 -0500

We advise to run first the TEdenovo pipeline but it is not compulsory. We suppose you begin by running the TEannot pipeline on the example provided in the directory "db/" rather than directly on your own genomic sequences. Thus, from now on, the project name is "DmelChr4".

Address of the bookmark: https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto

GeneMANIA

Jit — Mon, 22 Aug 2016 09:55:16 -0500

Faster, more accurate algorithms function prediction "GeneMANIA (Multiple Association Network Integration Algorithm)" have however been developed in recent years and are publicly available on the web, indicating the future direction of function prediction.

Address of the bookmark: http://genemania.org/

GeneValidator - Identify problems with predicted genes

Poonam Mahapatra — Fri, 26 Aug 2016 06:00:03 -0500

GeneValidator helps in identifing problems with gene predictions and provide useful information extracted from analysing orthologs in BLAST databases. The results produced can be used by biocurators and researchers who need accurate gene predictions.

If you would like to use GeneValidator on a few sequences, see our online GeneValidator Web App -http://genevalidator.sbcs.qmul.ac.uk.

If you use GeneValidator in your work, please cite us as follows:

Dragan M‡, Moghul MI‡, Priyam A, Bustos C & Wurm Y. 2016. GeneValidator: identify problems with protein-coding gene predictions. Bioinformatics, doi: 10.1093/bioinformatics/btw015.

Address of the bookmark: https://github.com/wurmlab/genevalidator

R-chie

Jit — Thu, 01 Sep 2016 11:47:24 -0500

R-chie allows you to make arc diagrams of RNA secondary structures, allowing for easy comparison and overlap of two structures, rank and display basepairs in colour and to also visualize corresponding multiple sequence alignments and co-variation information.
R4RNA is the R package powering R-chie, available for download and local use for more customized figures and scripting.

http://www.e-rna.org/r-chie/plot.cgi?eg=single

Address of the bookmark: http://www.e-rna.org/r-chie/plot.cgi?eg=single

Assembly tutorial PPT

Jit — Wed, 07 Sep 2016 03:12:53 -0500

Saved Cornell University assembly workshop PPT.

Reference:

http://cbsu.tc.cornell.edu/lab/doc/assembly_workshop_20150420_lecture1.pdf

FERMI

Jit — Fri, 09 Sep 2016 05:37:13 -0500

Fermi is a de novo assembler with a particular focus on assembling Illumina short sequence reads from a mammal-sized genome. In addition to the role of a typical assembler, fermi also aims to preserve heterozygotes which are often collapsed by other assemblers. Its ultimate goal is to find a minimal set of
unitigs to represent all the information in raw reads.

Fermi follows the overlap-layout-consensus paradigm and uses the FM-DNA-index (FMD-index) as the key data structure. It is inspired by the string graph assembler (Simpson and Durbin, 2010 and 2012) and has a similar workflow.

As a typical de novo assembler, fermi tends to produce contigs with slightly longer N50. However, the major weakness of fermi is the high misassembly rate. Although fermi provides a tool to fix misassemblies by using paired-end reads to achieve an accuracy comparable to other assemblers, this is not a favorable solution.

Fermi is designed to be used on a multi-core Linux machine with large shared memory. The easiest way to run fermi is to use the run-fermi.pl script. It generates a Makefile. The actual assembly is done by invoking make. Premature assembly processes can be resumed. Here is an example:

run-fermi.pl -dAPe ./fermi -p NA12878 -t16 -f18 reads*.fq.gz > NA12878.mak
make -f NA12878.mak -j16

Address of the bookmark: https://github.com/lh3/fermi

Genobuntu: A software package containing more than 70 software and packages oriented towards NGS and genome assembly

BioStar — Tue, 11 Dec 2018 05:15:57 -0600

Genobuntu is a software package containing more than 70 software and packages oriented towards NGS. In its current version, Genobuntu supports pre assembly tools, genome assemblers as well as post assembly tools.

Commonly used biological software and example script files for different assembly pipelines have also been provided, where the example script files can be updated to suit one’s experimental needs. Genobuntu attempts to reduce the amount of time and energy needed to build software workstations and it can also act as a good teaching source for a class room setting.

https://sourceforge.net/projects/genobuntu/

Address of the bookmark: https://sourceforge.net/projects/genobuntu/

Genome assembly tutorial "Genome Assembly for short and long reads"

Jit — Sat, 19 Jan 2019 17:29:53 -0600

In this lab we will perform de novo genome assembly of a bacterial genome. You will be guided through the genome assembly starting with data quality control, through to building contigs and analysis of the results. At the end of the lab you will know:

How to perform basic quality checks on the input data
How to run a short read assembler on Illumina data
How to run a long read assembler on Pacific Biosciences or Oxford Nanopore data
How to improve the accuracy of a long read assembly using short reads
How to assess the quality of an assembly

https://bioinformaticsdotca.github.io/high-throughput_biology_2017

Address of the bookmark: https://bioinformaticsdotca.github.io/high-throughput_biology_2017_module6_lab