BOL: Related items

Scarpa

Poonam Mahapatra — Wed, 13 Jul 2016 07:59:25 -0500

Scarpa is a stand-alone scaffolding tool for NGS data. It can be used together with virtually any genome assembler and any NGS read mapper that supports SAM format. Other features include support for multiple libraries and an option to estimate insert size distributions from data. Scarpa is available free of charge for academic and commercial use under the GNU General Public License (GPL).

See the user manual or the paper for more information about Scarpa. Click here for the supplementary material.

Address of the bookmark: http://compbio.cs.toronto.edu/hapsembler/scarpa.html

GRASS: a generic algorithm for scaffolding next-generation sequencing assemblies.

Abhimanyu Singh — Tue, 23 May 2017 05:20:32 -0500

GRASS (GeneRic ASsembly Scaffolder)-a novel algorithm for scaffolding second-generation sequencing assemblies capable of using diverse information sources. GRASS offers a mixed-integer programming formulation of the contig scaffolding problem, which combines contig order, distance and orientation in a single optimization objective. The resulting optimization problem is solved using an expectation-maximization procedure and an unconstrained binary quadratic programming approximation of the original problem. We compared GRASS with existing HTS scaffolders using Illumina paired reads of three bacterial genomes. Our algorithm constructs a comparable number of scaffolds, but makes fewer errors. This result is further improved when additional data, in the form of related genome sequences, are used.

Address of the bookmark: https://github.com/AlexeyG/GRASS

RePS: Repeat-masked Phrap with scaffolding, a WGS sequence assembler

Jit — Sat, 04 Jan 2020 01:08:09 -0600

RePS (Repeat-masked Phrap with scaffolding), a WGS sequence assembler, that explicitly identifies exact kmer repeats from the shotgun data and removes them prior to the assembly. The established software Phrap is used to compute meaningful error probabilities for each base. Clone-end-pairing information is used to construct scaffolds that order and orient the contigs. The updated version of RePS incorporates some of the ideas introduced by Phusion on clustering

More at

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC186573/

Address of the bookmark: ftp://ftp.genomics.org.cn/pub/ricedb/Tools/RePS/RePS-IBM-AIX.tar.gz

bpRNA: large-scale automated annotation and analysis of RNA secondary structure

Rahul Nayak — Wed, 23 May 2018 03:24:33 -0500

bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature.

The bpRNA code is written in perl and requires the Graph perl module. Several additional scripts for analysis are included. The source code is available at http://github.com/hendrixlab/bpRNA.

Address of the bookmark: http://github.com/hendrixlab/bpRNA

miREM: an expectation-maximization approach for prioritizing miRNAs associated with gene-set

Abhimanyu Singh — Thu, 16 Aug 2018 04:48:07 -0500

From list of genes... ... to microRNAs

Using established miRNA-interaction databases, miREM combines hypergeometric and expectation-maximization algorithms to sieve through your genetic data for microRNA signatures

https://bioinfo-csi.nus.edu.sg/mirem2/

Address of the bookmark: https://bioinfo-csi.nus.edu.sg/mirem2/

ONT assembly and Illumina polishing pipeline

Jit — Thu, 23 Nov 2017 10:13:42 -0600

This pipeline performs the following steps:

Assembly of nanopore reads using Canu.
Polish canu contigs using racon (optional).
Map a paired-end Illumina dataset onto the contigs obtained in the previous steps using BWA mem.
Perform correction of contigs using pilon and the Illumina dataset.

Address of the bookmark: https://github.com/nanoporetech/ont-assembly-polish

dnaPipeTE: de-novo assembly & annotation Pipeline for Transposable Elements

Rahul Nayak — Sat, 02 Dec 2017 18:25:44 -0600

dnaPipeTE (for de-novo assembly & annotation Pipeline for Transposable Elements), is a pipeline designed to find, annotate and quantify Transposable Elements in small samples of NGS datasets. It is very useful to quantify the proportion of TEs in newly sequenced genomes since it does not require genome assembly and works on small datasets (< 1X).

dnaPipeTE is developped by Clément Goubert, Laurent Modolo and the TREEP team of the LBBE: http://lbbe.univ-lyon1.fr/-Equipe-Elements-transposables-.html?lang=en
You can find the original publication in GBE here: https://academic.oup.com/gbe/article/7/4/1192/533768

output examples of quantification and TE landscape (relative age) produced by dnaPipeTE

Address of the bookmark: https://github.com/clemgoub/dnaPipeTE

Ra assembler - a de novo DNA assembler for third generation sequencing data

biogeek — Wed, 27 Dec 2017 20:36:54 -0600

Integration of the Ra assembler - a de novo DNA assembler for third generation sequencing data developed on Faculty of Electrical Engineering and Computing (FER), Ruder Boskovic Institute (RBI) and Genome Institute of Singapore (GIS).

Ra is in development since 2014 in the form of several separate components that used to be run individually.
This project aims to ease the usage of Ra by integrating it into a complete de novo assembly tool.

Unlike other state-of-the-art assemblers, Ra does not have an error correction step. Instead, it relies on detecting overlaps using a very sensitive and specific overlapper ("graphmap -w owler", https://github.com/isovic/graphmap) and constructing and reducing an overlap graph (Ra layout, https://github.com/mariokostelac/ra).

Address of the bookmark: https://github.com/mariokostelac/ra-integrate/

ASplice: a scalable and memory-efficient algorithm for de novo transcriptome assembly

Rahul Nayak — Tue, 03 Jul 2018 04:09:46 -0500

With increased availability of de novo assembly algorithms, it is feasible to study entire transcriptomes of non-model organisms. While algorithms are available that are specifically designed for performing transcriptome assembly from high-throughput sequencing data, they are very memory-intensive, limiting their applications to small data sets with few libraries. Texas A&M University researchers develop a transcriptome assembly algorithm that recovers alternatively spliced isoforms and expression levels while utilizing as many RNA-Seq libraries as possible that contain hundreds of gigabases of data. New techniques are developed so that computations can be performed on a computing cluster with moderate amount of physical memory. Availability – A software program that implements the algorithm is available at: http://faculty.cse.tamu.edu/shsze/asplice. Sze SH, Pimsler ML, Tomberlin JK, Jones CD, Tarone AM. (2017) A scalable and memory-efficient algorithm for de novo transcriptome assembly of non-model organisms. BMC Genomics 18(Suppl 4):387.

Address of the bookmark: http://faculty.cse.tamu.edu/shsze/asplice/

MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph

Jit — Wed, 14 Nov 2018 04:50:27 -0600

MEGAHIT is a single node assembler for large and complex metagenomics NGS reads, such as soil. It makes use of succinct de Bruijn graph (SdBG) to achieve low memory assembly. MEGAHIT can optionally utilize a CUDA-enabled GPU to accelerate its SdBG contstruction. The GPU-accelerated version of MEGAHIT has been tested on NVIDIA GTX680 (4G memory) and Tesla K40c (12G memory) with CUDA 5.5, 6.0 and 6.5. MEGAHIT v1.0 or greater also supports IBM Power PC and has been tested on IBM POWER8.

https://academic.oup.com/bioinformatics/article/31/10/1674/177884

Address of the bookmark: https://github.com/voutcn/megahit