BOL: Related items

Langya Virus Update !

Neel — Fri, 12 Aug 2022 05:31:10 -0500

https://www.ncbi.nlm.nih.gov/nuccore/OM101125,OM101126,OM101127,OM101128,OM101129,OM101130?

Zoonotic Henipavirus

https://pubmed.ncbi.nlm.nih.gov/35921459/

https://www.ncbi.nlm.nih.gov/nuccore/OM069646,,OM069567,OM069568,OM069569,OM069570,OM069571,OM069572,OM069573,OM069574,OM069575,OM069576,OM069577,OM069578,OM069579,OM069580,OM069581,OM069582,OM069583,OM069584,OM069585,OM069586,OM069587,OM069588,OM069589,OM069590,OM069591,OM069592,OM069593,OM069594,OM069595,OM069596,OM069597,OM069598,OM069599,OM069600,OM069601,OM069602,OM069603,OM069604,OM069605,OM069606,OM069607,OM069608,OM069609,OM069610,OM069611,OM069612,OM069613,OM069614,OM069615,OM069616,OM069617,OM069618,OM069619,OM069620,OM069621,OM069622,OM069623,OM069624,OM069625,OM069626,OM069627,OM069628,OM069629,OM069630,OM069631,OM069632,OM069633,OM069634,OM069635,OM069636,OM069637,OM069638,OM069639,OM069640,OM069641,OM069642,OM069643,OM069644,OM069645,OM069646

ViralQC: Checking If a Viral Genome Tells the Whole Story

LEGE — Tue, 08 Sep 2026 10:35:04 -0500

Imagine finding a mysterious piece of a puzzle and being told it belongs to a virus. Before studying it, you would want to know two things: Is the piece really viral, and how much of the puzzle is missing?

That is the problem ViralQC (https://github.com/ChengPENG-wolf/ViralQC) aims to solve.

Viral sequences recovered from metagenomic data can be incomplete or contaminated with microbial DNA. ViralQC uses information from both DNA sequences and predicted proteins to detect contamination and estimate how complete a viral contig is.

The authors compared ViralQC with CheckV and found that ViralQC performed particularly well for longer viral contigs, improving contamination detection and completeness estimation in several test cases.

Why does this matter? Because discovering a viral sequence is only the first step. If the sequence is contaminated or incomplete, downstream analyses—such as identifying viral functions or studying evolution—can be misleading.

ViralQC provides a useful quality check before researchers trust the viral genome they have discovered.

In a world where metagenomics is uncovering enormous numbers of unknown viruses, tools like ViralQC help us separate the real viral story from an incomplete or mixed-up one.

More at https://academic.oup.com/bioinformatics/article/42/Supplement_2/btag463/8767288

Coronavirus SARS-CoV-2

Shruti Paniwala — Wed, 17 Jun 2020 11:18:24 -0500

Used Nanographics Vj, our real-time molecular visualization and animation software, to create this video showing the structure of the virus. In the video, you can see the latest theory on how the RNA is organized inside of the virus particle.

On this page, you can download high resolution images of our renderings. We made them with transparent background, so that you can use it in your work. As the research progresses, we will keep updating the model as well as the images on this page, so stay tuned!

Address of the bookmark: https://nanographics.at/projects/sars-cov-2/

Unicycler: Hybrid assembly pipeline for bacterial genomes

Jit — Fri, 10 Nov 2017 03:58:27 -0600

Unicycler is an assembly pipeline for bacterial genomes. It can assemble Illumina-only read sets where it functions as a SPAdes-optimiser. It can also assembly long-read-only sets (PacBio or Nanopore) where it runs a miniasm+Racon pipeline. For the best possible assemblies, give it both Illumina reads and long reads, and it will conduct a hybrid assembly.

Address of the bookmark: https://github.com/rrwick/Unicycler

HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies

Jit — Tue, 15 May 2018 07:35:26 -0500

HapCUT2 is a maximum-likelihood-based tool for assembling haplotypes from DNA sequence reads, designed to "just work" with excellent speed and accuracy. We found that previously described haplotype assembly methods are specialized for specific read technologies or protocols, with slow or inaccurate performance on others. With this in mind, HapCUT2 is designed for speed and accuracy across diverse sequencing technologies, including but not limited to: NGS short reads (Illumina HiSeq) clone-based sequencing (Fosmid or BAC clones) SMRT reads (PacBio) Oxford Nanopore reads 10X Genomics Linked-Reads proximity-ligation (Hi-C) reads high-coverage sequencing (>40x coverage-per-SNP) using above technologies combinations of the above technologies (e.g. scaffold long reads with Hi-C reads) See below for specific examples of command line options and best practices for some of these technologies. NOTE: At this time HapCUT2 is for diploid organisms only. VCF input should contain diploid variants. If you use HapCUT2 in your research, please cite: Edge, P., Bafna, V. & Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. gr.213462.116 (2016). doi:10.1101/gr.213462.116

Address of the bookmark: https://github.com/vibansal/HapCUT2

transrate: Understanding your transcriptome assembly

Neel — Fri, 13 Jul 2018 07:49:26 -0500

Transrate is software for de-novo transcriptome assembly quality analysis. It examines your assembly in detail and compares it to experimental evidence such as the sequencing reads, reporting quality scores for contigs and assemblies. This allows you to choose between assemblers and parameters, filter out the bad contigs from an assembly, and help decide when to stop trying to improve the assembly.

Address of the bookmark: http://hibberdlab.com/transrate/index.html

ALLHiC: Phasing and scaffolding polyploid genomes based on Hi-C data

BioStar — Thu, 20 Dec 2018 12:03:32 -0600

The major problem of scaffolding polyploid genome is that Hi-C signals are frequently detected between allelic haplotypes and any existing stat of art Hi-C scaffolding program links the allelic haplotypes together. To solve the problem, we developed a new Hi-C scaffolding pipeline, called ALLHIC, specifically tailored to the polyploid genomes. ALLHIC pipeline contains a total of 5 steps: prune, partition, rescue, optimize and build.

Address of the bookmark: https://github.com/tangerzhang/ALLHiC/wiki

SvABA: Genome-wide detection of structural variants and indels by local assembly

Abhimanyu Singh — Mon, 21 Jan 2019 17:58:56 -0600

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

NextDenovo: string graph-based de novo assembler for TGS long reads

Jit — Sun, 05 Jan 2020 04:08:29 -0600

NextDenovo is a string graph-based de novo assembler for TGS long reads. It uses a "correct-then-assemble" strategy similar to canu, but requires significantly less computing resources and storages. After assembly, the per-base error rate is about 97-98%, to further improve single base accuracy, please use NextPolish.

NextDenovo contains two core modules: NextCorrect and NextGraph. NextCorrect can be used to correct TGS long reads with approximately 15% sequencing errors, and NextGraph can be used to construct a string graph with corrected reads. It also contains a modified version of minimap2 for adapting input and output and producing more sensitive and accurate dovetail overlaps, and some useful utilities (see here for more details).

Address of the bookmark: https://github.com/Nextomics/NextDenovo

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