Intro Biostatistics and Bioinformatics, lec 23 ChIP-seq and RNA-seq presented by Stuart Brown

A Brief History of Sequencing and Gene Expression. Tag Based Sequencing Approaches Serial Analysis of Gene Expression (SAGE) Cap Analysis of Gene Expression (CAGE) Massively parallel signature sequence (MPSS)

High resolution version - https://youtu.be/Wfxh9_fsRfo

Mapping RNA-seq reads visualized

The use of RNA-Seq to investigate RNA species and how they can regulate gene expression has enormous applicability toward the development of clinical diagnostics.

from RPubs - by Prasanth A S In order to explain the different methods of normalization and their problems with large array of data, which are assumed to have: most genes are not differentially expressed across conditions. the distribution of genes across the samples are roughly the same Question 3.8.1 Two sets of plot showing

Functional Analysis of Your RNAseq Data - Fiona McCarthy, University of Arizona The broad applicability and availability of RNASeq data has accelerated the generation of functional genomics data sets across a wide range of species. However, the end point of RNASeq analyses is to generate a list of differentially expressed genes; this list contributes little

Circadian rhythms are daily endogenous oscillations of behavior, metabolism, and physiology. At a molecular level, these oscillations are generated by transcriptional-translational feedback loops composed of core clock genes. In turn, core clock genes drive the rhythmic accumulation of downstream outputs-termed clock-controlled genes (CCGs)-whose rhythmic translation and function ultimately underlie daily oscillations at a cellular and

David Tse - Stanford University Symposium on Turbo Codes and Iterative Information Processing Bremen, Germany August 20, 2014 Joint work with Sreeram Kannan and Lior Pachter. Research supported by NSF Center for Science of Information

Transcriptome analysis based on a de novo assembly of next generation RNA sequences is now performed routinely in many laboratories. The generated results, including contig sequences, quantification figures, functional annotations and variation discovery outputs are usually bulky and quite diverse. This article presents a user oriented storage and visualisation environment permitting to explore the data

Researchers from the Istituto per le Applicazoni del Calcolo, CNR, Italy, have developed RNASeqGUI R package, a graphical user interface (GUI) for the identification of differentially expressed genes across multiple biological conditions. This R package includes some well known RNA-Seq tools, available at www.bioconductor.org. RNASeqGUI package is not just a collection of some known methods

Researchers at the Francis Crick Institute provide fundamental insights into early human development by single-cell RNA-sequencing of human and mouse preimplantation embryos. They elucidate conserved transcriptional programs along with those that are human-specific and validate their RNA-sequencing findings at the protein level. They identified several genes exclusively expressed in the human pluripotent epiblast including the

Analysis of differential gene expression by RNA sequencing (RNA-Seq) is frequently done using feature counts, i.e. the number of reads mapping to a gene. However, commonly used count algorithms (e.g. HTSeq) do not address the problem of reads aligning with multiple locations in the genome (multireads) or reads aligning with positions where two or more

RNA-Seq has become increasingly popular in transcriptome profiling. One aspect of transcriptome research is to quantify the expression levels of genomic elements, such as genes, their transcripts and exons. Acquiring a transcriptome expression profile requires genomic elements to be defined in the context of the genome. Multiple human genome annotation databases exist, including RefGene (RefSeq

Applications of deep-sequencing technologies in life science research and clinical diagnostics are rapidly expanding. Although fast data-processing algorithms exist, intuitive, portable data-evaluation solutions are still needed. Web tools have a history in bioinformatics of providing platform-independent, intuitive, barrier-free software solutions. Whereas in most scientific web tools a server performs intense calculations, the new HTML5 standard

The use of RNA-sequencing has garnered much attention in the recent years for characterizing and understanding various biological systems. However, it remains a major challenge to gain insights from...

RNA-Seq Viewer is a web application that enables users to visualize genome-wide expression data from NCBI's Sequence Read Archive (SRA) and Gene Expression Omnibus (GEO) databases. The application prototype was created by a small team during a three-day hackathon facilitated by NCBI at Brandeis University. The backend data pipeline was developed and deployed on a

from the Cornell Chronicle By Krishna Ramanujan A new Cornell study examines how genetic differences among individuals impact cellular stress, a first step in understanding how this stress response relates to human diseases, such as diabetes. The endoplasmic reticulum (ER) is a part of the cell involved in producing proteins, folding them for proper function

The continual maturation and increasing applications of next-generation sequencing technology in scientific research have yielded ever-increasing amounts of data that need to be effectively and efficiently analyzed and innovatively mined for new biological insights. Researchers at the Icahn School of Medicine have developed ngs.plot-a quick and easy-to-use bioinformatics tool that performs visualizations of the spatial

Mapping of short sequencing reads is a crucial step in the analysis of RNA sequencing (RNA-seq) data. ContextMap is an RNA-seq mapping algorithm that uses a context-based approach to identify the best alignment for each read and allows parallel mapping against several reference genomes. Here, researchers from Ludwig-Maximilians-Universität München present ContextMap 2, a new and improved