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Gene detection software tools | RNA sequencing data analysis

Locating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction.

Source text:
(Reid et al., 2014) SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models. BMC Bioinformatics.

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A highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. This approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci.
A web server designed for identifying protein-coding regions in expressed sequence tag (EST)-derived sequences. For query sequences with a hit in BLASTX, the program predicts the coding regions based on the translation reading frames identified in BLASTX alignments, otherwise, it predicts the most probable coding region based on the intrinsic signals of the query sequences. The output is the predicted peptide sequences in the FASTA format, and a definition line that includes the query ID, the translation reading frame and the nucleotide positions where the coding region begins and ends. The predicted protein sequences can then be used as the input for additional annotation tools, such as InterProScan, for identifying protein families, domains and functional sites, the Conserved Domain Search service for the detection of structural and functional domains, and SignalP for locating potential signal peptides.
A pipeline for unsupervised RNA-seq-based genome annotation that combines the advantages of GeneMark-ET and AUGUSTUS. As input, BRAKER1 requires a genome assembly file and a file in bam-format with spliced alignments of RNA-seq reads to the genome. First, GeneMark-ET performs iterative training and generates initial gene structures. Second, AUGUSTUS uses predicted genes for training and then integrates RNA-seq read information into final gene predictions. In our experiments, we observed that BRAKER1 was more accurate than MAKER2 when it is using RNA-seq as sole source for training and prediction. BRAKER1 does not require pre-trained parameters or a separate expert-prepared training step.
An efficient and fast genome scaffolding method, using proteins to scaffold genomes. The pipeline aims to recover protein-coding gene structures. We tested the method on human contigs; using human UniProt proteins as guides, the improvement on N50 size was 17% increase with an accuracy of ∼97%. PEP_scaffolder improved the proportion of fully covered proteins among all proteins, which was close to the proportion in the finished genome. The method provided a high accuracy of 91% using orthologs of distant species. Tested on simulated fly contigs, PEP_scaffolder outperformed other scaffolders, with the shortest running time and the highest accuracy.
Combines Genewise with our own homology-based method, AlignFS, to identify protein-coding regions and correct erroneous frame-shifts, suitable for subsequent phylogenetic analysis. We compared AlignWise against other open reading frame finding software and demonstrate that the AlignFS algorithm is more accurate than Genewise at correcting frame-shifts within an order. We show that AlignWise provides the greatest accuracy at higher evolutionary distances, out-performing both AlignFS and Genewise individually. AlignWise produces a single ORF per transcript and identifies and corrects frame-shifts with high accuracy. It is therefore well suited for analysing novel transcriptome assemblies and EST sequences in the absence of a reference genome.
Simplifies the execution and data integration from heterogeneous biological sequence analysis tools. Pegasys enables the execution and integration of heterogeneous biological sequence analyses. It allows users to create workflows using any combination of the available programs in this program by dragging/dropping and linking graphical icons that represent sequence analysis tools. It can execute and integrate results from ab initio gene prediction, pair-wise and multiple sequence alignments, RNA gene detection and masking of repetitive sequences.
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