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      Genotype–Environment mismatch of kelp forests under climate change

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          HISAT: a fast spliced aligner with low memory requirements.

          HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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            Is Open Access

            The variant call format and VCFtools

            Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API. Availability: http://vcftools.sourceforge.net Contact: rd@sanger.ac.uk
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              StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.

              Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.
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                Author and article information

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                Journal
                Molecular Ecology
                Mol Ecol
                Wiley
                0962-1083
                1365-294X
                August 2021
                June 16 2021
                August 2021
                : 30
                : 15
                : 3730-3746
                Affiliations
                [1 ]UWA Oceans Institute Crawley WA Australia
                [2 ]School of Biological Sciences University of Western Australia Crawley WA Australia
                [3 ]Institute of Marine Research His Norway
                [4 ]Simons Center for Quantitative Biology Cold Spring Harbor Laboratory Cold Spring Harbor, New York NY USA
                [5 ]New South Wales Department of Primary Industries Orange Agricultural Institute Orange NSW Australia
                [6 ]New South Wales Fisheries National Marine Science Centre Coffs Harbour NSW Australia
                [7 ]National Marine Science Centre Southern Cross University Coffs Harbour NSW Australia
                Article
                10.1111/mec.15993
                34018645
                ae6d018e-2cbe-4f62-a8a9-06bbcbd03784
                © 2021

                http://onlinelibrary.wiley.com/termsAndConditions#am

                http://onlinelibrary.wiley.com/termsAndConditions#vor

                http://doi.wiley.com/10.1002/tdm_license_1.1

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