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Bioinformatics Tools and Software in Clinical Research
Published in Rishabha Malviya, Pramod Kumar Sharma, Sonali Sundram, Rajesh Kumar Dhanaraj, Balamurugan Balusamy, Bioinformatics Tools and Big Data Analytics for Patient Care, 2023
Deepika Bairagee, Nitu Singh, Neelam Jain, Urvashi Sharma
Sean Eddy created HMMER, a free and widely used software suite for sequence analysis. Its primary use is to find the required proteins with homologous nucleotide sequences and to perform series alignments. It discovers homology by comparing a profile-HMM with either a single sequence or a sequence database. Sequences that score significantly higher on the profile-HMM than a null design are considered homologous to the sequences utilized to create the profile-HMM [27]. The program build is used to create profile-HMMs from a multiple sequence alignment within the HMMER bundle. The HMMER program’s profile-HMM execution is based on Krogh and colleagues’ work. HMMER is a system tool that has been ported to Linux, Windows, and the macOS. Pfam and InterPro are two well-known protein databases that rely heavily on HMMER. Several additional BI tools, including UGENE [27,28], utilize HMMER.
Unravelling the Oil and Gas Microbiome Using Metagenomics
Published in Kenneth Wunch, Marko Stipaničev, Max Frenzel, Microbial Bioinformatics in the Oil and Gas Industry, 2021
Assembled metagenomic data (i.e. contigs and MAGs) can subsequently be taken forward for annotation. Metagenome annotation refers to the identification of genomic features such as protein coding genes (CDS) as well as ribosomal RNA (rRNA) and transport RNA (tRNA) genes on the assembled metagenomic data. Protein coding genes can be predicted with tools like Prodigal (Hyatt et al., 2010), whereas barrnap (Seemann, 2013) and RNAmmer (Lagesen et al., 2007) can identify rRNA genes. For the identification of transport RNA genes, tRNAscan (Chan and Lowe, 2019) or ARAGORN (Laslett and Canback, 2004) can be used instead. Functional annotation of the predicted protein coding genes can be performed against databases such as KEGG, UniProt, NCBI, Pfam, and InterPro where functions from orthologous proteins and functional motifs and domains can be inferred. Standalone tools such as PROKA (Seemann, 2014), DFAST (Tanizawa et al., 2018) and MetaErg (Dong and Strous, 2019) combine the gene finding and functional annotation processes described above. In addition, web-based bioinformatics pipelines like IMG/M (Chen et al., 2019) and MG-RAST (Meyer et al., 2008) can provide gene finding and functional annotation capability as well as other advanced metagenome analysis tools.
Growth and genetic analysis of Pseudomonas BT1 in a high-thiourea environment reveals the mechanisms by which it restores the ability to remove ammonia nitrogen from wastewater
Published in Environmental Technology, 2022
Jingxuan Deng, Zhenxing Huang, Wenquan Ruan
To annotate the genes, the structure of the coding genes was predicted using Prodial [23] with the parameters of ‘-p None -g 11’ to obtain the whole CDS sequences. The tRNA and rRNA were predicted using tRNAscan-SE [24] with the parameters ‘-B -I -m lsu,ssu,tsu’, and RNAmmer [25] with the parameters ‘-S bac’, respectively. The ncRNA was predicted using infernal with the parameters ‘–cut_ga –rfam –nohmmonly’ by searching with the Rfam database, and only ncRNA with a predicted length longer than 80% of the sequence length in the database was obtained. To annotate the functions of genes, the protein sequences were mapped to the InTerPro, TIGRFAMs, Pfam, Kyoto Encyclopaedia of Genes and Genomes (KEGG), Gene Ontology (GO) and COG databases.
Assays and enumeration of bioaerosols-traditional approaches to modern practices
Published in Aerosol Science and Technology, 2020
Maria D. King, Ronald E. Lacey, Hyoungmook Pak, Andrew Fearing, Gabriela Ramos, Tatiana Baig, Brooke Smith, Alexandra Koustova
More detailed identification of biodiversity and microbiome is possible with the advancement and combination of powerful tools. The most commonly used method to identify bacterial taxonomy is the 16S rRNA gene sequencing as this gene is present in most bacteria, its function has not changed over time, and it has a fairly long base pair of 1500 bp for analysis (Janda and Abbott 2007). This is advantageous in many ways, such as identifying the species of ambiguous isolates, but it also has its limitations. Some identified species strains are outdated and may not be accurate anymore, the sequencing resolution does not clearly distinguish between certain bacterial strains, and there are still some errors in public databases of sequences. Databases, including the open access GenBank (National Center for Biotechnology Information [NCBI]), the European Nucleotide Archive, the DNA Data Bank of Japan, and the UniProt, Protein Data Bank, Ensembl and InterPro are powerful tools that allow the search and alignment of nucleotide or protein sequences (Boratyn et al. 2013; Wheeler and Bhagwat 2007). Shotgun sequencing is another method of high-throughput sequencing that uses untargeted sequencing over a microbial genome to compile whole genomes. Although it has some limitations, such as high cost, lack of valid annotations, and genome biases, shotgun sequencing results in metagenomics that enables complex microorganisms to be identified and analyzed (Quince et al. 2017). Next-generation sequencing (NGS) platforms enable other types of microbiome analysis and identification, such as whole-genome sequencing, total RNA sequencing, and methylation sequencing (Illumina 2016). The NGS technologies enable the generation of millions of reads from aerosol samples to analyze the genomics and transcriptomics (for RNA analysis), and have been successfully used in several bioaerosol studies (Womack et al. 2015; Shin et al. 2015). Metagenomic approaches enable comprehensive determination of the diversity and metabolic potential of the collected airborne organisms.