BOL: Related items

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

Salzberg lab

Mon, 15 May 2017 05:14:01 -0500

We are a computational biology lab that develops novel methods for analysis of DNA and RNA sequences. Our research includes software for aligning and assembling RNA-seq data, whole-genome assembly, and microbiome analysis. We work closely with biomedical scientists to apply these methods to current problems arising in a broad spectrum of biological and medical research areas. We’re also part of the Center for Computational Biology, a group of 20+ faculty members and their labs at Johns Hopkins working on computational, statistical, and mathematical methods that can turn massive genomic data sets into biologically and clinically useful information.

https://salzberg-lab.org/

AccNET

Jitendra Narayan — Fri, 07 Oct 2016 05:22:11 -0500

AccNET is a Perl application that presents a new way to study the accessory genome of a given set of organisms. Using the proteomes of these organisms, AccNET create a bipartite network compatible with common network analysis platforms. AccNET collects phylogenetic and functional information in a network improving the analysis capability. Networks offer a new perspective of organism organization through elements acquired by horizontal gene transfers and not constricted by hierarchical structures.

More at https://www.youtube.com/watch?v=vdGuy1GAJrQ

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

A5-miseq

Jit — Thu, 18 Aug 2016 04:05:23 -0500

_A5-miseq_ is a pipeline for assembling DNA sequence data generated on the Illumina sequencing platform. This README will take you through the steps necessary for running _A5-miseq_.

Point to note:

There are many situations where A5-miseq is not the right tool for the job. In order to produce accurate results, A5-miseq requires Illumina data with certain characteristics. A5-miseq will likely not work well with Illumina reads shorter than around 80nt, or reads where the base qualities are low in all or most reads before 60nt. A5-miseq assumes it is assembling homozygous haploid genomes. Use a different assembler for metagenomes and heterozygous diploid or polyploid organisms. Use a different assembler if a tool like FastQC reports your data quality is dubious. You have been warned! Datasets consisting solely of unpaired reads are not currently supported.

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

valet

Jit — Thu, 22 Sep 2016 04:27:09 -0500

VALET is a pipeline for performing de novo validation of metagenomic assemblies. VALET checks a number of properties that should hold true for a correct assembly (e.g., mate-pairs are aligned at the correct distance from each other in the assembly, the depth of coverage is fairly uniform along contigs, etc.). The violations of these invariants are reported allowing one to pinpoint areas that were potentially mis-assembled, or to compare the quality of different assemblies. For comparing multiple assemblies of the same data-sets, VALET also reports an overall estimate of the likelihood a particular assembly is correct.

Home Page:

VALET code repository

Address of the bookmark: https://www.cbcb.umd.edu/software/valet

vcfR

Archana Malhotra — Fri, 19 Aug 2016 07:38:24 -0500

Most variant calling pipelines result in files containing large quantities of variant information. The variant call format (vcf) is an increasingly popular format for this data. The format of these files and their content is discussed in the vignette ‘vcf data.’ These files are typically intended to be post-processed (i.e., filtered) as an attempt to remove false positives or otherwise problematic sites. The R package vcfR provides tools to facilitate this filtering as well as to visualize the effects of choices made during this process.

Address of the bookmark: https://cran.r-project.org/web/packages/vcfR/vignettes/visualization_1.html

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

Sushi: An R/Bioconductor package for visualizing genomic data

Jit — Wed, 31 Aug 2016 08:29:12 -0500

Sushi: An R/Bioconductor package for visualizing genomic data

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/Sushi/inst/doc/Sushi.pdf

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

Structural variants PPT

Jit — Wed, 07 Sep 2016 03:16:09 -0500

1000 Genomes data tutorial at ASHG

Structural variants presentation by

Jan Korbel

European Molecular Biology Laboratory (EMBL) Heidelberg Genome Biology Research Unit

Reference:

https://www.genome.gov/pages/research/der/1000genomesprojecttutorials/structuralvariants-jankorbel.pdf