The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

The graph shows the changes in the g-index of ^ and the corresponding percentile for the sake of comparison with the entire literature. g-index is a scientometric index similar to g-index but put a more weight on the sum of citations. The g-index of a journal is g if the journal has published at least g papers with total citations of g2.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

Efficiently querying specific genomic regions is fundamental in bioinformatics, which allows to extract relevant feature information from large genomic datasets. While existing tools provide query capabilities, they are limited to interval manipulation and do not natively support linked interval data or complex relationship between genomic features. We introduce genogrove, a hybrid graph data structure designed to facilitate scalable interval queries. We demonstrate how it serves as an efficient interval search structure for large-scale datasets and lays the foundation for more advanced genomic analyses, supporting a wide range of applications in bioinformatics.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

Aperture index files for hg19&hbv, including:hg19_hbv_ref.cihg19_hbv_ref.kmhg19_hbv_ref.long.kmhg19_hbv_ref.spaced.kmhg19_hbv_ref.tt

Our indexed full gene catalog (merged oral and gut) for use with the Diamond protein aligner (https://github.com/bbuchfink/diamond). You can download this file and use Diamond to search it by sequence.

Aperture index files for hg38&hbv, including:hg38_hbv_ref.cihg38_hbv_ref.kmhg38_hbv_ref.long.kmhg38_hbv_ref.spaced.kmhg38_hbv_ref.tt

the bam index file

The graph shows the changes in the g-index of ^ and the corresponding percentile for the sake of comparison with the entire literature. g-index is a scientometric index similar to g-index but put a more weight on the sum of citations. The g-index of a journal is g if the journal has published at least g papers with total citations of g2.

Index for gzipped Vector Observatory specimen genotypes, arm 2L. Made available under the Ag1000G terms of use: https://www.malariagen.net/data/terms-use/ag1000g-terms-use

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

Global Bioinformatics market size was USD 12.76 Billion in 2022 and it is forecasted to reach USD 29.32 Billion by 2030. Bioinformatics Industry's Compound Annual Growth Rate will be 10.4% from 2023 to 2030. What are the driving factors for the Bioinformatics market?

The primary factors propelling the global bioinformatics industry are advances in genomics, rising demand for protein sequencing, and rising public-private sector investment in bioinformatics. Large volumes of data are being produced by the expanding use of next-generation sequencing (NGS) and other genomic technologies; these data must be analyzed using advanced bioinformatics tools. Furthermore, the global bioinformatics industry may benefit from the development of emerging advanced technologies. However, the bioinformatics discipline contains intricate algorithms and massive amounts of data, which can be difficult for researchers and demand a lot of processing power. What is Bioinformatics?

Bioinformatics is related to genetics and genomics, which involves the use of computer technology to store, collect, analyze, and disseminate biological information, and data, such as DNA and amino acid sequences or annotations about these sequences. Researchers and medical professionals use databases that organize and index this biological data to better understand health and disease, and in some circumstances, as a component of patient care. Through the creation of software and algorithms, bioinformatics is primarily used to extract knowledge from biological data. Bioinformatics is frequently used in the analysis of genomics, proteomics, 3D protein structure modeling, image analysis, drug creation, and many other fields.

Journal of theoretical and applied computer science Abstract & Indexing - ResearchHelpDesk - Journal of Theoretical and Applied Computer Science is published by the Computer Science Commision, operating within the Gdansk Branch of Polish Academy of Sciences and located in Szczecin, Poland. JTACS is an open access journal, publishing original research and review papers from the variety of subdiscplines connected to theoretical and applied computer science, including the following: Artificial intelligence Computer modelling and simulation Data analysis and classification Pattern recognition Computer graphics and image processing Information systems engineering Software engineering Computer systems architecture Distributed and parallel processing Computer systems security Web technologies Bioinformatics Abstract and indexing Doaj (Dicretroy of open access journals) Index copurnicus Baztech Google scholar

Next Generation Sequencing (NGS) analysis of Cell-Free DNA provides valuable insights into a spectrum of pathogenic species (particularly bacterial) in blood. Patients with Sepsis often face problems like delays in treatment regimens (combination or cocktail of antibiotics) due to the long turnaround time (TAT) of classical and standard blood culture procedures. NGS gives results with lower TAT along with high-depth coverage. The use of NGS may be a possible solution to deciding treatment regimens for patients without losing precious time and more accurately possibly saving lives.

Our curated dataset is of bacterial species or strains detected along with their genome size in 107 AML patients diagnosed with Sepsis clinically. Cell-free DNA profiles of patients were built and sequencing was done in Illumina (NovaSeq and NextSeq). Bioinformatic analysis was performed using two classification algorithms namely kraken2 and kaiju. For kraken2 based classification reference bacterial index developed by Carlo Ferravante et al (Zenodo 2020) (link: https://zenodo.org/records/4055180) was used, while for kaiju-based classification reference database named "nr_euk" dated "2023-05-10" (link: https://bioinformatics-centre.github.io/kaiju/downloads.html) was used.

Genome size annotation is important in metagenomics since for the use of depth of coverage (abundance), genome size is required. In metagenomic classification algorithms like kraken/kraken2 and kaiju output computes reads assigned only and not abundance. In kaiju, the problem is more complicated since the reference database does not have a fasta file but only an index file from which alignment is done.

To address the above challenges to compute "depth of coverage" or simply abundance, we build a Genome size annotator tool (https://github.com/patkarlab/Genome-Size-Annotation) which provides genome size for each species detected given its taxid is available. In this tool, the NCBI Datasets tool, NCBI Genome API check tool, and Data Mining from AI search engines like perplexity.ai are used.

We have curated two datasets

Kraken2 dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kraken_genome_annotation"
Kaiju dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kaiju_genome_annotation"

*Please note that for kraken2 curated dataset, we used data mining from the AI search engine perplexity.ai while for kaiju we did not use perplexity, ai, and any species whose genome size was not found was labeled "NA"

Index file corresponding to BAM file http://figshare.com/articles/Example_BAM_file/1460736

Journal of Applied Biology and Biotechnology Acceptance Rate - ResearchHelpDesk - The Journal of Applied Biology & Biotechnology is a open access, peer-reviewed journal published by Open Science Publishers LLP (registers under section 12(1) of LLP Act 2008). The journal publish on Bi-monthly basis (6 issue per year) and available in both online and print format. Aims & Scope Journal of Applied Biology and Biotechnology is a peer-reviewed, open access journal, dedicated to publication of review articles, original research, short communications on applied researches in following fields of Cell biology, Biology, Developmental biology, Structural biology, Microbiology, Molecular biology, Biochemistry, Biotechnology, Food Science, Medicinal Plants, Ethnobotany, Environmental biology, Marine biology, Viorology, Bioinformatics, Biophysics, Evolutionary biology, Plant Science, Plant pathology, Plant physiology, Plant breeding, Nematology, Agriculture Science and Agronomy. Journal Abstracting and Indexing details Biological Abstracts, BIOSIS Previews, CAB Abstracts, Chemical Abstracts, J-Gate, Google Scholar, Indian Science Abstracts, Tropical Diseases Bulletin, Science Central, Index Medicus for South-East Asia Region, Review of Aromatic and Medicinal Plants, ScopeMed, EBSCO, Geneva Foundation for Medical Education and Research, Abstracts on Hygiene and Communicable Diseases, Global Health, Plant Genetic Resources Abstracts, Index Copernicus, Genamics JournalSeek.

Reformatted PHROGs database

Reformatting of the PHROGs database (https://phrogs.lmge.uca.fr/index.php) to allow it to be used within Prokka directly and output the PHROGS annotations to the “product” description in genbank files. All annotations are maintained from the PHROGs database set.

File is called all_PHROGs.tar.gz

Place in prokka-1.13-2/db/hmm/ to use with prokka

Contained within this repository are worked examples for the assembly and annotation of a bacteriophage genome.

Full detailed instructions are provided as part of the manuscript “Phage Genome annotation: where to begin and end”

File: SRR13108336_Illumina.tar.gz contains all the files produced from the intermediary steps in the assembly and annotation of a phage genome from single end Illumina reads. Originally deposited by Milhaven et al (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8407768/) and used as an exemplar

File: nanopore_example.tar.gz contains all the files produced from the intermediary steps in the assembly and annotation of a phage genome from nanopore data. Originally deposited by D`Souza et al (https://journals.asm.org/doi/10.1128/MRA.00730-20)

This workflow adapts the approach and parameter settings of Trans-Omics for precision Medicine (TOPMed). The RNA-seq pipeline originated from the Broad Institute. There are in total five steps in the workflow starting from:

1. Read alignment using STAR which produces aligned BAM files including the Genome BAM and Transcriptome BAM.
2. The Genome BAM file is processed using Picard MarkDuplicates producing an updated BAM file containing information on duplicate reads (such reads can indicate biased interpretation).
3. SAMtools index is then employed to generate an index for the BAM file, in preparation for the next step.
4. The indexed BAM file is processed further with RNA-SeQC which takes the BAM file, human genome reference sequence and Gene Transfer Format (GTF) file as inputs to generate transcriptome-level expression quantifications and standard quality control metrics.
5. In parallel with transcript quantification, isoform expression levels are quantified by RSEM. This step depends only on the output of the STAR tool, and additional RSEM reference sequences.

For testing and analysis, the workflow author provided example data created by down-sampling the read files of a TOPMed public access data. Chromosome 12 was extracted from the Homo Sapien Assembly 38 reference sequence and provided by the workflow authors. The required GTF and RSEM reference data files are also provided. The workflow is well-documented with a detailed set of instructions of the steps performed to down-sample the data are also provided for transparency. The availability of example input data, use of containerization for underlying software and detailed documentation are important factors in choosing this specific CWL workflow for CWLProv evaluation.

This dataset folder is a CWLProv Research Object that captures the Common Workflow Language execution provenance, see https://w3id.org/cwl/prov/0.5.0 or use https://pypi.org/project/cwl

Horse breeders rely heavily on pedigrees for identification of ancestry in breeding stock. Inaccurate pedigrees may erroneously assign individuals to false lineages or breed memberships resulting in wrong estimates of inbreeding and coancestry. Moreover, discrepancies in pedigree records can lead breeders seeking to limit inbreeding into making misguided breeding decisions. Genome-wide SNPs provide a quantitative tool to aid in the resolution of lineage assignments and the calculation of genomic measures of relatedness. The aim of this project was to pilot a comparison between pedigree and genomic relatedness and inbreeding measures in a herd of 36 pedigreed Egyptian Arabian horses genotyped using the Equine SNP70 platform (Geneseek, Inc.). Moreover, we sought to estimate the minimum number of markers sufficient for genomic inbreeding calculations. Pedigree inbreeding values were moderately correlated with genomic inbreeding values (r = 0.406), whereas genomic relationships and pedigree relationships have a high correlation (r = 0.77). Although first degree relationships were successfully reconstructed, more distant relationships were difficult to resolve. Multi-dimensional scaling and clustering analysis agreed with within-herd pedigree information. In comparing the herd to a reference sample of United States, Polish, and Egyptian Arabian horses, the herd's historically recorded Egyptian lineage was successfully recovered. We conclude that genomic estimates of inbreeding and relationships are superior to their pedigree counterparts. They can be thus utilized in conservation of valuable lines of livestock, and in breeds at risk for loss of genomic diversity. We also postulate a minimum of 2000 markers in linkage equilibrium to be used for inbreeding estimation.