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Artificial Intelligence in Systems Biology
Published in P. Kaliraj, T. Devi, Artificial Intelligence Theory, Models, and Applications, 2021
S. Dhivya, S. Hari Priya, R. Sathishkumar
Algorithm for read processing such as Next Generation Sequencing (NGS) Data QC toolkit, Cutdapt, and FastX have been used to trim out the low-grade adapters in sequencing. To identify the sequence similarity, the processed reads were compared with the reference genome, followed by multiple sequence alignment using NovoAlign, BWA, STAR, TWAP, TOPHAT, and RNA sequencing. GATKs (Genome Analysis Tool Kits) are widely utilized for intel alignment base quality recalibration, variant calling, and also to find precise mutants from sequencing, mapping, genomic libraries, and sample enrichment. Numerous germline and somatic mutant calling algorithms were developed for detecting low-frequency mutants from the complex system through an error correction technique. Genomic mutants fall under four major groups like insertion, deletion (del), structural variants like duplication, translocation, copy number variants, and SNVs (Single Nucleotide Variants). SNV is ≤10 bp and it is to check non-reference nucleotides from the pile of sequence that covers each position. The tools used for germline variant calling involves GAP and MAG integrated by GATK, whereas CRISP and Thunder are used for the analysis of variant calling and pooled samples data.
A New Era of Nuclear Criticality Experiments: The First 10 Years of Godiva IV Operations at NCERC
Published in Nuclear Science and Engineering, 2021
Joetta Goda, Caiser Bravo, Theresa Cutler, Travis Grove, David Hayes, Jesson Hutchinson, George McKenzie, Alexander McSpaden, William Myers, Rene Sanchez, Jessie Walker
Figure 11 contains excess reactivity versus temperature data generated from 2013 through June 2020. Excess reactivity refers to the the reactivity measured when all control elements (except the burst rod) are inserted. The plots show the negative temperature coefficient of reactivity for Godiva. The temperatures for Resistance Temperature Detector (RTD) 1 in Ring 104 and RTD 2 in the safety block were averaged. There are three sets of data corresponding to different configurations. For the “No Tophat” data set, a linear fit shows a temperature coefficient of −0.31 ¢/°C. The data were recorded over the course of 5 years during burst operations. The wide range of temperatures reflects facility conditions and in some cases residual temperature from a prior burst the same day. The experimental purpose and configuration of the machine, samples, external detectors, etc., varied between operations and the resulting data show a large scatter.
Transcriptome analysis of Takifugu obscurus liver in response to acute retene exposure
Published in Journal of Environmental Science and Health, Part A, 2020
Shulun Jiang, Di-an Fang, Dongpo Xu
The raw sequence data were deposited in the National Center for Biotechnology Information (NCBI) under the bio-project number PRJNA627538 (Sample names of “Rexi” represent “RET treatment” in the present study). The raw reads were processed using SOAP nuke.[30] Adaptor sequences, reads containing more than 10% unknown bases, and reads with a quality score less than 20 were eliminated. The remaining, clean reads were aligned to the reference genome of Takifugu rubripes (access number in GenBank: GCF000180615.1) using TopHat (version 2.0.11).[31] Bowtie2 v2.2.1[32] was used to generate an index file based on the reference genome. TopHat was run as follows: -N 2 -read-gap-length 3 -read-edit-dist 3 -read-realign-edit-dist 0 -report-secondary-alignments -coverage-search -microexon-search -library-type fr-unstranded -b2-sensitive. Finally, the aligned reads were assembled using Cufflinks default settings,[33] which created a Reference Annotation Based Transcript (RABT) assembly using the annotation file of reference genome (GCF_000180615.1_FUGU5_genomic.gff, downloaded from ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/180/615/GCF_000180615.1_FUGU5).