BOL: Related items

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

RNA-Bloom: a fast and memory-efficient de novo transcript sequence assembler

LEGE — Thu, 09 Jul 2020 03:13:06 -0500

RNA-Bloom is a fast and memory-efficient de novo transcript sequence assembler. It is designed for the following sequencing data types:

single-end/paired-end bulk RNA-seq (strand-specific/agnostic)
paired-end single-cell RNA-seq (strand-specific/agnostic)
nanopore RNA-seq (PCR cDNA/direct cDNA/direct RNA)

Written by Ka Ming Nip ✉️

Address of the bookmark: https://github.com/bcgsc/RNA-Bloom

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

SDA: Long-read sequence and assembly of segmental duplications

Jit — Tue, 05 Mar 2019 10:00:57 -0600

Segmental Duplication Assembler (SDA; https://github.com/mvollger/SDA) constructs graphs in which paralogous sequence variants define the nodes and long-read sequences provide attraction and repulsion edges, enabling the partition and assembly of long reads corresponding to distinct paralogs.

https://github.com/mvollger/SDA

Address of the bookmark: https://www.nature.com/articles/s41592-018-0236-3

StringTie Transcript assembly and quantification for RNA-Seq

Jit — Tue, 09 Jun 2020 05:21:11 -0500

StringTie is a fast and highly efficient assembler of RNA-Seq alignments into potential transcripts. It uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus. Its input can include not only alignments of short reads that can also be used by other transcript assemblers, but also alignments of longer sequences that have been assembled from those reads. In order to identify differentially expressed genes between experiments, StringTie's output can be processed by specialized software like Ballgown, Cuffdiff or other programs (DESeq2, edgeR, etc.).

Address of the bookmark: https://ccb.jhu.edu/software/stringtie/

MEC: Contig Misassembly Correction

BioStar — Tue, 04 Feb 2020 23:40:49 -0600

MEC, to identify and correct misassemblies in contigs. Firstly, MEC takes fragment coverage as the feature to detect the candidate misassemblies. Then, it can distinguish a large number of false positives from the candidate misassemblies based on the distribution of paired-end reads and the statistical analysis of GC-contents. We apply MEC to four real contig datasets, and carry out experiments to analyze the influence of MEC on scaffolding results, which shows that MEC can reduce misassemblies effectively and result in quantitative improvements in scaffolding quality. MEC is publicly available for download at https://github.com/bioinfomaticsCSU/MEC.

Address of the bookmark: https://github.com/bioinfomaticsCSU/MEC

SvABA: Structural variation and indel detection by local assembly

Jit — Tue, 10 Mar 2020 07:52:15 -0500

SvABA is a method for detecting structural variants in sequencing data using genome-wide local assembly. Under the hood, SvABA uses a custom implementation of SGA (String Graph Assembler) by Jared Simpson, and BWA-MEM by Heng Li. Contigs are assembled for every 25kb window (with some small overlap) for every region in the genome. The default is to use only clipped, discordant, unmapped and indel reads, although this can be customized to any set of reads at the command line using VariantBam rules. These contigs are then immediately aligned to the reference with BWA-MEM and parsed to identify variants. Sequencing reads are then realigned to the contigs with BWA-MEM, and variants are scored by their read support.

Address of the bookmark: https://github.com/walaj/svaba

CSA: A high-throughput chromosome-scale assembly pipeline for vertebrate genomes

Jit — Wed, 10 Mar 2021 06:13:49 -0600

The pipeline can use information from scaffolded assemblies (for example from HiC or 10X Genomics), or even from diverged (~65-100 Mya) reference genomes for ordering the contigs and thus support the assembly process. This typically results in improved contig N50 when compared to current state of the art methods.

For smaller vertebrate genomes (~1 Gbp) chromosome scale assemblies can be achieved within 12h on high-end Desktop computers (Intel i7, 12 CPU threads, 128 GB RAM). Larger mammalian genomes (~3Gbp) can be processed within 15-18 h on server equipment (Xeon, 96 CPU threads, 1TB RAM).

Address of the bookmark: https://github.com/HMPNK/CSA2.6

CrossMap

Jitendra Narayan — Mon, 08 Feb 2016 15:47:00 -0600

CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between different assemblies (such as Human hg18 (NCBI36) <> hg19 (GRCh37), Mouse mm9 (MGSCv37) <> mm10 (GRCm38)).

It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.

CrossMap is designed to liftover genome coordinates between assemblies. It’s not a program for aligning sequences to reference genome.

We do not recommend using CrossMap to convert genome coordinates between species.

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

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