Nanopre and PacBio based Genome Assembly

Hi-C experiments explore the 3D structure of the genome, generating terabases of data to create high-resolution contact maps. Here, we introduce Juicer, an open-source tool for analyzing terabase-scale Hi-C datasets. Juicer allows users without a computational background to transform raw sequence data into normalized contact maps with one click. Juicer produces a hic file containing compressed contact matrices at many resolutions, facilitating visualization and analysis at multiple scales. Structural features, such as loops and domains, are automatically annotated.

NextDenovo is a string graph-based de novo assembler for long reads (CLR, HiFi and ONT). It uses a “correct-then-assemble” strategy similar to canu (no correction step for PacBio HiFi reads), but requires significantly less computing resources and storages. After assembly, the per-base accuracy is about 98-99.8%, to further improve single base accuracy, try NextPolish.

IgCaller is a python program designed to fully characterize the immunoglobulin gene rearrangements and oncogenic translocations in lymphoid neoplasms. It was originally developed to work with WGS data but it has been extended to work with WES and high-coverage, capture-based NGS data.

MUMmer is a system for rapidly aligning entire genomes. The current version (release 4.x) can find all 20 base pair maximal exact matches between two bacterial genomes of ~5 million base pairs each in 20 seconds, using 90 MB of memory, on a typical 1.8 GHz Linux desktop computer.

Whole Genome Sequencing (WGS) provides a deep insight into the DNA sequence of humans, animals, plants, and microbial genomes, with data analysis at the individual or population level. SNP/INDEL/CNV/SV and other variants of the genome can be fully analysed. Our sequencing analysis enables the identif wication of somatic and germline mutations as well as customized patterns of cancers and other diseases. (Novogene)

The cellranger vdj pipeline can be used to analyze sequencing data produced from Chromium Next GEM Single Cell 5' V(D)J libraries. It takes FASTQ files for V(D)J libraries and performs sequence assembly and paired clonotype calling. The pipeline uses the Chromium Cell Barcodes (also called 10x Barcodes) and UMIs to assemble V(D)J transcripts per cell. Clonotypes and CDR3 sequences are output as a .vloupe file which can be loaded into Loupe V(D)J Browser. Visit the What is Cell Ranger page to learn more about Cell Ranger for Immune Profiling. (10X Genomics)

CD-HIT was originally a protein clustering program. The main advantage of this program is its ultra-fast speed. It can be hundreds of times faster than other clustering programs, for example, BLASTCLUST. Therefore it can handle very large databases, like NR. The 1st version of this program, CD-HI, was published and released in 2001. The 2nd version, called CD-HIT, was published in 2002 with significant improvements. Since 2004, CD-HIT has been hosted at bioinformatics.org as an open source project. Current CD-HIT package can perform various jobs like clustering a protein database, clustering a DNA/RNA database, comparing two databases (protein or DNA/RNA), generating protein families, and many others.

The blog post delves into the realm of PacBio sequencing, elucidating its significance in the world of next-generation sequencing. Contrasting PacBio with other sequencing technologies, such as Illumina's short-read and Oxford Nanopore's long-read sequencing, the article highlights the unique advantages and challenges posed by each. The comprehensive PacBio data analysis pipeline is elucidated step by step, from raw data collection to final report generation. A special section is dedicated to comparing tools used in the PacBio pipeline, offering insights into their strengths and limitations.

Gene regulation plays a crucial role in various biological processes, and understanding its mechanisms is essential for advancing our knowledge in life sciences. The Advent of ATAC-seq, a powerful tool for mapping open chromatin regions, has revolutionized the study of gene regulation by providing insight into the regulatory elements that control gene expression. This review aims to provide an overview of the current state of ATAC-seq applications in various fields, including stem cell biology, cancer research, neurobiology, immunology, plant biology, microbiology, drug discovery, personalized medicine, and synthetic biology. We discuss the advantages and limitations of ATAC-seq and highlight its potential for identifying new therapeutic targets and developing personalized therapies. Overall, ATAC-seq has proven to be a valuable tool for unlocking gene regulation and has the potential to lead to significant breakthroughs in many areas of life science research.

scRNA-Seq: makers explore

RNA-Seq stands for RNA sequencing, a revolutionary technique that helps scientists understand the expression of genes within a cell. In traditional RNA-Seq, we study the averaged gene expression of thousands of cells, but this approach has its limitations. It’s like trying to understand the flavor profile of a smoothie by tasting it – you know the overall taste, but you can’t pinpoint the individual fruits that contribute to it.

Alternative splicing research is important for understanding the diversity of gene expression and regulation, as it enables the production of multiple protein isoforms from a single gene, allowing cells to generate complex functional diversity and adapt to changing environmental conditions. Who said this?

RNA-seq expression normalization is the process of adjusting the raw gene expression counts to account for differences in sequencing depth and other technical factors. It is important to perform normalization to enable comparisons between samples and increase the accuracy and reproducibility of downstream analyses. Common normalization methods include TPM, FPKM, and DESeq. Who sad this?

Gene Set Enrichment Analysis (GSEA) is a powerful tool for interpreting gene expression data in the context of predefined biological pathways and gene sets. It allows researchers to identify enriched gene sets, discover new relationships between genes, and gain insights into the underlying biological mechanisms. Who sad this?