IsoSeq long-read analysis

IsoSeq De-Novo Isoform Discovery with OmicsBox

Introduction The IsoSeq sequencing method produces full-length transcripts using Single Molecule, Real-Time (SMRT) Sequencing. Long read lengths allow sequencing of full-length transcripts up to 10 kb or longer, removing the need for transcript assembly or inferencing. The IsoSeq bioinformatics pipeline processes the data into high-quality consensus transcript sequences that enable accurate isoform annotation and open reading frame prediction. OmicsBox implements

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Gene Prediction Augustus OmicsBox

Eukaryotic Gene Finding with OmicsBox

Introduction Given a new genome, one of the first and most important tasks is determining the structure of its protein-coding genes. Ab initio gene prediction algorithms play a critical role because they produce gene structures quickly, inexpensively, and remarkably reliable. In OmicsBox, the Eukaryotic Gene Finding application is based on AUGUSTUS, which is one of the most accurate programs for

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Clustering redundant transcripts in OmicsBox with CD-HIT

De novo transcriptome assemblies are required to analyze RNA-seq data from a species for which there is no reference genome. However, with the advancement of next-generation sequencing technologies, the amount of available sequencing data is growing exponentially. Because of this, assembly algorithms often generate a large number of transcripts. Removing redundancy from such data could be crucial for reducing storage space,

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Long-Reads Assembly with OmicsBox

Third-generation DNA sequencing technologies allows scientist to generate longer sequence reads, which can be used in whole-genome sequencing projects to yield better repeat resolution and more contiguous genome assemblies. However, although long-read sequencing technologies can produce genomes with long contiguity, the relatively high error rate of long reads has made it challenging to generate highly accurate final sequences. OmicsBox now

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Busco OmicsBox Screenshot

Exploring transcriptome completeness in OmicsBox with BUSCO

De novo transcriptome assemblies are required to analyze RNA-seq data from a species for which there is no reference genome. Once the assembly is complete, researchers need to know how good it is or compare the quality of similar assemblies generated by different parameters. There are several ways to characterize the quality of transcriptome assemblies. A good metric of assembly

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Predict coding regions within transcripts in OmicsBox with TransDecoder

Most transcripts assembled from eukaryotic and prokaryotic RNA-Seq data are expected to code for proteins. The most practical procedure to identify likely coding transcripts is a sequence homology search, such as by BLASTX, against sequences from a well-annotated and related species. Predicting coding regions is crucial to determine the molecular role that transcripts play in the cell. Unfortunately, such well-annotated

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Hybrid Genome Assembly in OmicsBox with SPAdes

DNA sequencing is the process of determining the nucleic acid sequence in DNA, and it is the technology by which the genome of a species can be characterized. Despite the advent of next-generation sequencing, current DNA sequencing technologies cannot read whole genomes at once, but rather reads small pieces of between 20 and 30.000 bases, depending on the technology used.

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SPAdes OmicsBox ScreenShot

New Genome Analysis Features

Release OmicsBox version 1.2 (24th of October, 2019) We are happy to announce the following updates for the genome analysis module. New feature includes a new DNA-Seq de novo assembly strategy based on SPAdes.More details can be found below as well as in the online user manual and Genome Analysis Module website. DNA-Seq de Novo Assembly: SPAdes SPAdes (St Petersburg genome

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Busco OmicsBox Screenshot

New Transcriptomics Features

Release OmicsBox version 1.2 (24th of October, 2019) We are happy to announce the following updates for the transcriptomics module. New features include Completeness Assessment and Predict Coding Regions. More details can be found below as well as in the online user manual and Transcriptomics Module website. Completeness Assessment The Completeness Assessment functionality provides quantitative measures for the assessment of transcriptome assembly completeness, based on

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