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Use Cases, Reviews, Tutorials

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Monocle3 in OmicsBox: scRNA Seq trajectory interference

OmicsBox’s Single Cell Data Analysis encompasses a comprehensive range of steps, spanning read quantification, pre-processing, clustering, differential expression analysis, and even advanced analyses like trajectory analysis and functional enrichment. This blog post will focus on general trajectory inference using Monocle3 within OmicsBox. Pseudotime Analysis with Monocle3 Monocle3, developed by the Trapnell lab, is a scRNA-Seq data analysis toolkit written in

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Pathway Analysis with OmicsBox: Analyzing a Salamander Limb Regeneration Transcriptomics Dataset

The functional interpretation of differentially expressed genes obtained from RNA-seq data is a critical step in elucidating the intricate biological mechanisms underlying complex biological processes. Pathway analysis has emerged as a powerful approach to gaining insights into the coordinated interactions among genes within biological pathways. However, a major challenge in pathway analysis lies in the availability of annotation data, which

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Decoding Long-Read Sequenced Transcriptomes: FLAIR vs. StringTie2

Transcriptome reconstruction is a challenging bioinformatic problem. The development of long-read sequencing technologies has made it easier to solve this issue thanks to the possibility of having transcripts entirely contained in a read. In addition, different algorithms have emerged to generate transcriptomes from long-read datasets, such as FLAIR and StringTie2. Both tools can be used for transcriptome reconstruction with long-read data.

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genetic variaton

Genetic Variation Exploration in Non-Model Organisms with OmicsBox

The study of genetic variation is fundamental in genomics research as it enables the comprehension of mutation causes and effects in the diversity of life. However, research in this field is well-developed in model organisms, while non-model organisms lack sufficient guidance for a consistent variant analysis pipeline. OmicsBox is a robust tool for investigating genetic variation in non-model organisms, facilitating

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Easy GWAS with OmicsBox

GWAS with OmicsBox

  Genome-Wide Association Studies (GWAS) are a valuable approach to investigating the relationship between genetic variations and phenotypic traits. However, due to many different analysis options, performing GWAS can be challenging, especially for researchers without a strong background in statistics or bioinformatics. OmicsBox’s Genetic Variation Module provides a solution by offering a user-friendly interface to powerful analysis tools. It includes

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Quality Assessment of DNA-Seq De Novo Assemblies using QUAST

Genetic Variation studies often involve analyzing samples from a previously studied species. For instance, it is of interest to examine genomes of various cultivars, strains, or populations of the same species. In such cases, it may be necessary to perform de novo DNA-Seq assembly to obtain the genome of the sequenced organisms, even if a reference genome is available. To

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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 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 nearby

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Cell-Type Identification via Functional Enrichment Analysis of Single-Cell RNA-Seq Data

  Single-cell RNA sequencing (scRNA-seq) has proven its usefulness in deciphering biological processes. Since it allows the study of samples at the cell level, it provides a more detailed view of the underlying biology.  A nearly mandatory step in any scRNA-seq analysis is to identify the cell types present in the analyzed samples so further biological insights can be obtained.

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