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Single Cell RNA-Seq analysis of Arabidopsis thaliana roots

  Single-cell RNA-Seq (scRNA-Seq) analysis has been widely used to investigate animal tissue heterogeneity, principally with human and mouse data. However, this technique is increasingly being used to study plant tissues as well with comparable results. Despite the challenges it faces due to the plant cell wall, researchers have been able to perform single-cell analysis with plant protoplasts, bringing the

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Population Structure Analysis

Population Structure Analysis with OmicsBox

Population structure analysis is a statistical method used to infer the genetic composition and ancestry proportions of individuals within a population. This type of study allows us to understand genetic variation within and between populations. Building admixture models provides insights into evolutionary processes, migration patterns, and even disease susceptibility. ADMIXTURE, now available in OmicsBox, makes this analysis easier by providing a

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Gene Set Analysis of GWAS data with MAGMA

The pursuit of genetic advancement in agricultural crops is a nuanced and ongoing scientific endeavor. The advent of Genome-Wide Association Studies (GWAS) marked a significant stride, offering a window into the intricate interplay between genetic variants and phenotypes. Yet, the scope of insights garnered through GWAS can be further enriched when coupled with refined analytical tools. The Gene Set Analysis

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Long-Read Sequence Alignment with Minimap2

  Sequence alignment is an essential element of genomics research, playing a significant role in aiding scientists in deciphering the intricacies of genetic data. When confronted with the task of aligning long reads generated by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT) with precision and efficiency, scientists commonly opt for Minimap2 as their preferred tool (it is the most

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STARsolo Single-cell Quantification in OmicsBox

  What is scRNA-Seq Quantification? The first step for any Single-cell RNA-Seq analysis is to perform the quantification or, in other words, to obtain the gene expression values by cell. It is the input data for further analysis like Clustering, Trajectory, Cell-Cell Communication, Differential Expression, etc. Briefly, in order to obtain the expression values the sequencing reads are aligned to

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cutadapt omicsbox

Demultiplexing with Cutadapt in OmicsBox

Barcoding and demultiplexing in high-throughput sequencing experiments Barcoding, or indexing, is a widely used strategy in high-throughput sequencing experiments. This method enables the multiplexing of numerous samples in a single sequencing run by adding a unique DNA sequence to each sample before sequencing. During sequencing, the barcodes are read along with the DNA fragments, allowing the identification and split of

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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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