Tag: long-read Sequencing data

RepeatHMM v2.0.3 – Estimation of Repeat Counts on Microsatellites from long-read sequencing data

RepeatHMM v2.0.3

:: DESCRIPTION

RepeatHMM is a novel computational tool to detect any microsatellites (including trinucleotide repeats in trinucleotide repeat disorders (TRD)) from given long reads for a subject of interests. It is able to accurately estimate estimate expansion counts according to the evaluation performance on both simulation data and real data.

::DEVELOPER

Wang Genomics Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

RepeatHMM

:: MORE INFORMATION

Citation

Liu Q, Zhang P, Wang D, Gu W, Wang K.
Interrogating the “unsequenceable” genomic trinucleotide repeat disorders by long-read sequencing.
Genome Med. 2017 Jul 18;9(1):65. doi: 10.1186/s13073-017-0456-7. PMID: 28720120; PMCID: PMC5514472.

NanoCaller 0.3.3 – Variant Calling tool for long-read Sequencing data

NanoCaller 0.3.3

:: DESCRIPTION

NanoCaller is a computational method that integrates long reads in deep convolutional neural network for the detection of SNPs/indels from long-read sequencing data. NanoCaller uses long-range haplotype structure to generate predictions for each SNP candidate variant site by considering pileup information of other candidate sites sharing reads. Subsequently, it performs read phasing, and carries out local realignment of each set of phased reads and the set of all reads for each indel candidate variant site to generate indel calling, and then creates consensus sequences for indel sequence prediction.

::DEVELOPER

Wang Genomics Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Pyton

:: DOWNLOAD

NanoCaller

:: MORE INFORMATION

Citation

Ahsan, Umair and Liu, Qian and Wang, Kai.
NanoCaller for accurate detection of SNPs and small indels from long-read sequencing by deep neural networks.
bioRxiv 2019.12.29.890418; doi: https://doi.org/10.1101/2019.12.29.890418