BOL: Related items

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

Merfin: improved variant filtering, assembly evaluation and polishing via k-mer validation

Neel — Sun, 03 Apr 2022 20:35:19 -0500

Merfin, a k-mer based variant-filtering algorithm for improved accuracy in genotyping and genome assembly polishing. Merfin evaluates each variant based on the expected k-mer multiplicity in the reads, independently of the quality of the read alignment and variant caller’s internal score. Merfin increased the precision of genotyped calls in several benchmarks, improved consensus accuracy and reduced frameshift errors when applied to human and nonhuman assemblies built from Pacific Biosciences HiFi and continuous long reads or Oxford Nanopore reads, including the first complete human genome. Moreover, we introduce assembly quality and completeness metrics that account for the expected genomic copy numbers.

More at https://www.nature.com/articles/s41592-022-01445-y

Address of the bookmark: https://github.com/arangrhie/merfin

TRITEX, a computational pipeline for chromosome-scale assembly of plant genomes

LEGE — Fri, 14 Feb 2025 10:53:48 -0600

This is the documentation of TRITEX, a computational pipeline for chromosome-scale assembly of plant genomes. It was developed in the research group Domestication Genomics at the Leibniz Institute of Plant Genetics and Crop Research (IPK) Gatersleben.

Address of the bookmark: https://tritexassembly.bitbucket.io/

RNA-Seq De novo Assembly Using Trinity

Surabhi Chaudhary — Wed, 23 Mar 2016 05:53:46 -0500

Trinity, developed at the Broad Institute and the Hebrew University of Jerusalem, represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes. Briefly, the process works like so:

Inchworm assembles the RNA-seq data into the unique sequences of transcripts, often generating full-length transcripts for a dominant isoform, but then reports just the unique portions of alternatively spliced transcripts.
Chrysalis clusters the Inchworm contigs into clusters and constructs complete de Bruijn graphs for each cluster. Each cluster represents the full transcriptonal complexity for a given gene (or sets of genes that share sequences in common). Chrysalis then partitions the full read set among these disjoint graphs.
Butterfly then processes the individual graphs in parallel, tracing the paths that reads and pairs of reads take within the graph, ultimately reporting full-length transcripts for alternatively spliced isoforms, and teasing apart transcripts that corresponds to paralogous genes.

More at https://github.com/trinityrnaseq/trinityrnaseq/wiki

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Download Trinity here.

Build Trinity by typing 'make' in the base installation directory.

Assemble RNA-Seq data like so:

 Trinity --seqType fq --left reads_1.fq --right reads_2.fq --CPU 6 --max_memory 20G

Find assembled transcripts as: 'trinity_out_dir/Trinity.fasta'

Address of the bookmark: https://github.com/trinityrnaseq/trinityrnaseq/wiki

RACA: Reference-Assisted Chromosome Assembly

Priya Singh — Wed, 06 Apr 2016 09:29:50 -0500

Rreference-Assisted Chromosome Assembly (RACA), an algorithm to reliably order and orient sequence scaffolds generated by NGS and assemblers into longer chromosomal fragments using comparative genome information and paired-end reads.

http://www.ncbi.nlm.nih.gov/pubmed/23307812

http://bioen-compbio.bioen.illinois.edu/RACA/

Address of the bookmark: http://bioen-compbio.bioen.illinois.edu/RACA/

DISCOVAR

Abhimanyu Singh — Mon, 18 Apr 2016 11:59:16 -0500

DISCOVAR is a new variant caller and DISCOVAR de novo a new genome assembler, both designed for state-of-the-art data. Their inputs are chosen to optimize quality while keeping costs low. Currently it takes as input Illumina reads of length 250 or longer — produced on MiSeq or HiSeq 2500 — and from a single PCR-free library. These data enable a level of completeness and continuity that was not previously possible.

DISCOVAR can call variants on a region by region basis, potentially tiling an entire large genome. DISCOVAR variant calling is under active development and transitioning to VCF.

DISCOVAR de novo can generate de novo assemblies for both large and small genomes. It currently does not call variants.

More at https://www.broadinstitute.org/software/discovar/blog/?page_id=14

Address of the bookmark: https://www.broadinstitute.org/software/discovar/blog/

Blobology

Jit — Mon, 13 Jun 2016 10:18:33 -0500

Tools for making blobplots or Taxon-Annotated-GC-Coverage plots (TAGC plots) to visualise the contents of genome assembly data sets as a QC step

Blaxter Lab, Institute of Evolutionary Biology, University of Edinburgh

Goal: To create blobplots or Taxon-Annotated-GC-Coverage plots (TAGC plots) to visualise the contents of genome assembly data sets as a QC step.

This repository accompanies the paper:
Blobology: exploring raw genome data for contaminants, symbionts and parasites using taxon-annotated GC-coverage plots. Sujai Kumar, Martin Jones, Georgios Koutsovoulos, Michael Clarke, Mark Blaxter
(submitted 2013-10-01 to Frontiers in Bioinformatics and Computational Biology special issue : Quality assessment and control of high-throughput sequencing data).

It contains bash/perl/R scripts for running the analysis presented in the paper to create a preliminary assembly, and to create and collate GC content, read coverage and taxon annotation for the preliminary assembly, which can be visualised, such as Figure 2a from the paper showing TAGC plots/blobplots for Caenorhabditis sp. 5:

Address of the bookmark: https://github.com/blaxterlab/blobology

HGA

Jit — Tue, 29 Nov 2016 07:25:53 -0600

HGA tool version 1.0 This tool helps to apply the Hierarchical Genome Assembly (HGA) method. The tool will apply: 1. Partitioning a given reads dataset into a given number of partitions. 2. Assembling each partitions using a pre-specified assembler (Velvet or SPAdes in this version) and using a given kmer size. 3. Merging all the assemblies of the partition. 4. Combining all the assemblies of the partition (using velvet with kmer value of 31). 5. Finaly, re-assembling the whole dataset with the merged contigs or the combined contigs, using a given kmer size.

https://github.com/aalokaily/Hierarchical-Genome-Assembly-HGA

Address of the bookmark: https://github.com/aalokaily/Hierarchical-Genome-Assembly-HGA