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      Snake Venomics: Fundamentals, Recent Updates, and a Look to the Next Decade

      Toxins
      MDPI AG

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          Abstract

          Venomic research, powered by techniques adapted from proteomics, transcriptomics, and genomics, seeks to unravel the diversity and complexity of venom through which knowledge can be applied in the treatment of envenoming, biodiscovery, and conservation. Snake venom proteomics is most extensively studied, but the methods varied widely, creating a massive amount of information which complicates data comparison and interpretation. Advancement in mass spectrometry technology, accompanied by growing databases and sophisticated bioinformatic tools, has overcome earlier limitations of protein identification. The progress, however, remains challenged by limited accessibility to samples, non-standardized quantitative methods, and biased interpretation of -omic data. Next-generation sequencing (NGS) technologies enable high-throughput venom-gland transcriptomics and genomics, complementing venom proteomics by providing deeper insights into the structural diversity, differential expression, regulation and functional interaction of the toxin genes. Venomic tissue sampling is, however, difficult due to strict regulations on wildlife use and transfer of biological materials in some countries. Limited resources for techniques and funding are among other pertinent issues that impede the progress of venomics, particularly in less developed regions and for neglected species. Genuine collaboration between international researchers, due recognition of regional experts by global organizations (e.g., WHO), and improved distribution of research support, should be embraced.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            Fast and accurate long-read alignment with Burrows–Wheeler transform

            Motivation: Many programs for aligning short sequencing reads to a reference genome have been developed in the last 2 years. Most of them are very efficient for short reads but inefficient or not applicable for reads >200 bp because the algorithms are heavily and specifically tuned for short queries with low sequencing error rate. However, some sequencing platforms already produce longer reads and others are expected to become available soon. For longer reads, hashing-based software such as BLAT and SSAHA2 remain the only choices. Nonetheless, these methods are substantially slower than short-read aligners in terms of aligned bases per unit time. Results: We designed and implemented a new algorithm, Burrows-Wheeler Aligner's Smith-Waterman Alignment (BWA-SW), to align long sequences up to 1 Mb against a large sequence database (e.g. the human genome) with a few gigabytes of memory. The algorithm is as accurate as SSAHA2, more accurate than BLAT, and is several to tens of times faster than both. Availability: http://bio-bwa.sourceforge.net Contact: rd@sanger.ac.uk
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              NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy

              The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database is a collection of genomic, transcript and protein sequence records. These records are selected and curated from public sequence archives and represent a significant reduction in redundancy compared to the volume of data archived by the International Nucleotide Sequence Database Collaboration. The database includes over 16 000 organisms, 2.4 × 106 genomic records, 13 × 106 proteins and 2 × 106 RNA records spanning prokaryotes, eukaryotes and viruses (RefSeq release 49, September 2011). The RefSeq database is maintained by a combined approach of automated analyses, collaboration and manual curation to generate an up-to-date representation of the sequence, its features, names and cross-links to related sources of information. We report here on recent growth, the status of curating the human RefSeq data set, more extensive feature annotation and current policy for eukaryotic genome annotation via the NCBI annotation pipeline. More information about the resource is available online (see http://www.ncbi.nlm.nih.gov/RefSeq/).
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                TOXIB7
                Toxins
                Toxins
                MDPI AG
                2072-6651
                April 2022
                March 30 2022
                : 14
                : 4
                : 247
                Article
                10.3390/toxins14040247
                35448856
                60dc1d3d-9a60-4426-9311-3c69e41defc7
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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