Gtf format

Shifted by 0, 1 and 2 nucleotides:seq GeneMark.hmm2 CDS 1 474 24.07 + 0 ... partial 10 ...seq GeneMark.hmm2 CDS 2 475 24.07 + 0 ... partial 10 ...seq GeneMark.hmm2 CDS 3 476 24.07 + 0 ... partial 10 ...Incomplete CDS can be predicted inside a sequence. Assembly gaps, represented by long stretches of letters 'N' in assembly, can lead to incomplete CDS structures inside the sequence. GeneMark.hmm2 can predict such incomplete genes. For example:seq GeneMark.hmm2 CDS 11 472 23.7 + 0 ... partial 11 ...The same rule of starting and ending at full codon applies to incomplete internal genes.CDS coordinates include position of the stop codon in GFF and GFF3 formats. Only in case of incomplete in 3' end CDS a stop is not present. GTF format specification excludes stop codon from CDS coordinates. GeneMark.hmm-2 deviates from GTF standard and always includes stop codon into CDS coordinates.Score of CDS feature is log-odd score for CDS in GeneMark.hmm-2 (not P or E value).Column 9 in GFF, GTF and GFF3In GFF Column 9 in original GFF format was optional.In GeneMark.hmm-2 column 9 in GFF is formatted using following rules:* Key and value pairs are separated by space* Key/values pares are separated by semicolon ';'* Order of key/value pairs is arbitraryFor example:seq GeneMark.hmm2 CDS 1 474 24.07 + 0 gene_id 1; partial 10; gene_type atypical; gc 45, length 474;The following keys/values are currently supported:gene_id number;In GeneMarkS-2 Gene ID is an integer value starting with "1" and incremented by "1" across all the contigs in the input file.partial label;GFF* format has no dedicated rule for labeling incomplete genes. Thus, information about incomplete CDS status is stored with key 'partial' and one of the values '01', '10' or '11'. The value indicates the side where CDS is incomplete:'01' incomplete from right'10' incomplete from left'11' incomplete from both sidesAttention:Order of the key/value pairs in column 9 (attribute) is arbitrary and may change between the versions.Additional key/value pairs can be introduced in new versions of the codeFormatting rules for CDS split by linearization of circular chromosome where not specified in the described formats.These rules may be introduced in the new versions of the code.

Note: This repo contains the code for the training phase of GeneMarkS-2 only. To download and use the complete program, please visit topaz.gatech.eduGeneMarkS-2Article Name: Modeling leaderless transcription and atypical genes results in more accurate gene prediction in prokaryotes.Authors: Alex Lomsadze^, Karl Gemayel^, Shiyuyun Tang and Mark BorodovskyAffiliation: Georgia Institute of TechnologyGroup Website: topaz.gatech.eduPubMed: www.ncbi.nlm.nih.gov/pubmed/29773659/InstallStructure: GeneMarkS-2 is made up of four components:gms2.pl : Controls the entire GeneMarkS-2 algorithmbiogem : Implements the training stages of GeneMarkS-2gmhmmp2 : Implements the prediction stages of GeneMarkS-2compp : Used for checking for convergence by comparing consecutive prediction filesSee the INSTALL file for more detail.ExecutionTo run GeneMarkS-2, simply execute the perl script 'gms2.pl' by invoking 'perl gms2.pl'.This will print out the usage message showing all possible input parameters (see below).GeneMarkS-2 with its default parameters can be run by:perl gms2.pl -s sequence.fasta --genome-type TYPE --output OUTWhere 'sequence.fasta' is the FASTA file containing the sequence.And TYPE is bacteria, archaea or auto (auto detection of domain)UsageUsage: gms2.pl --seq SEQ --genome-type TYPEBasic Options:--seq File containing genome sequence in FASTA format--genome-type Type of genome: archaea, bacteria, auto (default: auto)--gcode Genetic code (default: auto. Supported: 11, 4, 25 and 15)--output Name of output file (default: gms2.lst)--format Format of output file (default: lst)--ext Name of file with external information in GFF format (PLUS mode of GMS2)--fnn Name of output file that will hold nucleotide sequences of predicted genes--faa Name of output file that will hold protein sequences of predicted genes--gid Change gene ID format--species Name of the species to use inside the model file (default: unspecified)--advanced-options Show the advanced optionsVersion: 1.14_1.24_licGeneMarkS-2 OtputGeneMarkS-2 uses GeneMark.hmm-2 as a core gene finder.Final output is generated by GeneMark.hmm-2.GeneMark.hmm-2 OutputCoordinates of predicted genes can be saved in GFF, GTF, GFF3 and LST formats.LST format is custom human readable format developed at GaTech for GeneMark.hmm.LST is default output format in GeneMark.hmm-2.GFF, GTF and GFF3 formats were developed and have been widely used for description of genes in eukaryotic species.These formats are not yet widely adopted for gene description of prokaryotic species. Almost all prokaryotic gene findersuse by default custom formats and also support one or another variant of GFF format with gene finder specific modifications.GTF and GFF3 are formats derived from original GFF format.GFF, GTF and GFF3 formats use similar 8 first mandatory columns.Deviation from standard in GeneMark.hmm-2 in first 8 columns:Incomplete CDS can be present in genomes due to gaps in sequence assembly or linearization of circular chromosome. Most frequently incomplete CDSi's are found at the beginning or at the end of the contig. Incomplete CDS's predicted by GeneMark.hmm-2 always start and end with full codon. Thus, all predicted CDS in GFF* formats will have phase zero. For example, these three lines describe incomplete gene on direct (plus) strand

The cellranger pipeline outputs unfiltered (raw) and filtered feature-barcode matrices in two file formats: the Market Exchange Format (MEX), which is described on this page, and Hierarchical Data Format (HDF5), which is described in detail here.Each element of the feature-barcode matrix is the number of UMIs associated with a feature (row) and a barcode (column):TypeDescriptionUnfiltered feature-barcode matrixContains every barcode from the fixed list of known-good barcode sequences that has at least one read. This includes background and cell-associated barcodes. count: outs/raw_feature_bc_matrix/ multi: outs/multi/count/raw_feature_bc_matrix/Filtered feature-barcode matrixContains only detected cell-associated barcodes. count: outs/filtered_feature_bc_matrix/ multi: outs/per_sample_outs/count/sample_filtered_feature_bc_matrix/Prior to Cell Ranger 3.0, the output matrix file format was different. In particular, the file genes.csv has been replaced by features.csv.gz to account for Feature Barcode technology, and the matrix and barcode files are now gzipped. In Cell Ranger v7.0 and later, the cellranger multi pipeline produces a filtered feature-barcode matrix called sample_filtered_feature_bc_matrix, previously called sample_feature_bc_matrixFor sparse matrices, the matrix is stored in the Market Exchange Format (MEX). It contains gzipped TSV files with feature and barcode sequences corresponding to row and column indices respectively. For example, the matrices output may look like:cd /home/jdoe/runs/sample345/outstree filtered_feature_bc_matrixfiltered_feature_bc_matrix ├── barcodes.tsv.gz ├── features.tsv.gz └── matrix.mtx.gz0 directories, 3 filesFeatures correspond to row indices. For each feature, the feature ID and name are stored in the first and second column of the (unzipped) features.tsv.gz file, respectively. The third column identifies the type of feature, which will be one of Gene Expression, Antibody Capture, CRISPR Guide Capture, Multiplexing Capture, or CUSTOM, depending on the feature type. Below is a minimal example features.tsv.gz file showing data collected for three genes and two antibodies.gzip -cd filtered_feature_bc_matrix/features.tsv.gzENSG00000141510 TP53 Gene ExpressionENSG00000012048 BRCA1 Gene ExpressionENSG00000139687 RB1 Gene ExpressionCD3_GCCTGACTAGATCCA CD3 Antibody CaptureCD19_CGTGCAACACTCGTA CD19 Antibody CaptureFor Gene Expression data, the ID corresponds to gene_id in the annotation field of the reference GTF. Similarly, the name corresponds to gene_name in the annotation field of the reference GTF. If no gene_name field is present in the reference GTF, gene name is equivalent to gene ID. Similarly, for Antibody Capture and CRISPR Guide Capture data, the id and name are taken from the first two columns of the Feature Reference CSV file.For multi-species experiments, gene IDs and names are prefixed with the genome name to avoid name collisions between genes of different species e.g., GAPDH becomes hg19_GAPDH and Gm15816 becomes mm10_Gm15816.Barcode sequences correspond to column indices:gzip -cd filtered_feature_bc_matrices/barcodes.tsv.gzAAACCCAAGGAGAGTA-1AAACGCTTCAGCCCAG-1AAAGAACAGACGACTG-1AAAGAACCAATGGCAG-1AAAGAACGTCTGCAAT-1AAAGGATAGTAGACAT-1AAAGGATCACCGGCTA-1AAAGGATTCAGCTTGA-1AAAGGATTCCGTTTCG-1AAAGGGCTCATGCCCT-1Each barcode sequence includes a suffix with

UCSC Table Browser do notcontain isoform-gene relationship information. However, if you use theUCSC Genes annotation track, this information can be recovered bydownloading the knownIsoforms.txt file for the appropriate genome.To prepare the reference sequences, you should run thersem-prepare-reference program. Runrsem-prepare-reference --helpto get usage information or visit the rsem-prepare-referencedocumentation page. Build RSEM references using RefSeq, Ensembl, or GENCODE annotationsRefSeq and Ensembl are two frequently used annotations. For human andmouse, GENCODE annotaions are also available. In this section, we showhow to build RSEM references using these annotations. Note that it isimportant to pair the genome with the annotation file for eachannotation source. In addition, we recommend users to use the primaryassemblies of genomes. Without loss of generality, we use human genome asan example and in addition build Bowtie indices.For RefSeq, the genome and annotation file in GFF3 format can be foundat RefSeq genomes FTP:ftp://ftp.ncbi.nlm.nih.gov/genomes/refseq/For example, the human genome and GFF3 file locate at the subdirectoryvertebrate_mammalian/Homo_sapiens/all_assembly_versions/GCF_000001405.31_GRCh38.p5. GCF_000001405.31_GRCh38.p5is the latest annotation version when this section was written.Download and decompress the genome and annotation files to your working directory:ftp://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_mammalian/Homo_sapiens/all_assembly_versions/GCF_000001405.31_GRCh38.p5/GCF_000001405.31_GRCh38.p5_genomic.fna.gzftp://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_mammalian/Homo_sapiens/all_assembly_versions/GCF_000001405.31_GRCh38.p5/GCF_000001405.31_GRCh38.p5_genomic.gff.gzGCF_000001405.31_GRCh38.p5_genomic.fna contains all top levelsequences, including patches and haplotypes. To obtain the primaryassembly, run the following RSEM python script:rsem-refseq-extract-primary-assembly GCF_000001405.31_GRCh38.p5_genomic.fna GCF_000001405.31_GRCh38.p5_genomic.primary_assembly.fnaThen type the following command to build RSEM references:rsem-prepare-reference --gff3 GCF_000001405.31_GRCh38.p5_genomic.gff \ --trusted-sources BestRefSeq,Curated\ Genomic \ --bowtie \ GCF_000001405.31_GRCh38.p5_genomic.primary_assembly.fna \ ref/human_refseqIn the above command, --trusted-sources tells RSEM to only extracttranscripts from RefSeq sources like BestRefSeq or Curated Genomic. Bydefault, RSEM trust all sources. There is also an--gff3-RNA-patterns option and its default is mRNA. Setting--gff3-RNA-patterns mRNA,rRNA will allow RSEM to extract all mRNAsand rRNAs from the genome. Visit herefor more details.Because the gene and transcript IDs (e.g. gene1000, rna28655)extracted from RefSeq GFF3 files are hard to understand, it isrecommended to turn on the --append-names option inrsem-calculate-expression for better interpretation ofquantification results.For Ensembl, the genome and annotation files can be found atEnsembl FTP.Download and decompress the human genome and GTF files:ftp://ftp.ensembl.org/pub/release-83/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gzftp://ftp.ensembl.org/pub/release-83/gtf/homo_sapiens/Homo_sapiens.GRCh38.83.gtf.gzThen use the following command to build RSEM references:rsem-prepare-reference --gtf Homo_sapiens.GRCh38.83.gtf \ --bowtie \ Homo_sapiens.GRCh38.dna.primary_assembly.fa \ ref/human_ensemblIf you want to use GFF3 file instead, which is unnecessary and notrecommended, you should add option --gff3-RNA-patterns transcriptbecause mRNA is replaced by transcript