Be. Genomics: open source platform for Bioinformatics. Gaining comprehensive biological insight into the transcriptome by performing a broad- spectrum RNA- seq analysis. RNA- sequencing (RNA- seq) is an essential technique for transcriptome studies, hundreds of analysis tools have been developed since it was debuted. Although recent efforts have attempted to assess the latest available tools, they have not evaluated the analysis workflows comprehensively to unleash the power within RNA- seq. Here we conduct an extensive study analysing a broad spectrum of RNA- seq workflows. Surpassing the expression analysis scope, our work also includes assessment of RNA variant- calling, RNA editing and RNA fusion detection techniques. Specifically, we examine both short- and long- read RNA- seq technologies, 3. Reference. Ge. Seq: versatile and accurate annotation of organelle genomes. We have developed the web application Ge. Seq (https: //chlorobox. In contrast to existing tools, Ge. Introduction to Biological Membranes. Biological membranes are composed of lipid, protein and carbohydrate that exist in a fluid state. Biological membranes are the. The oxidative phosphorylation page provides a discussion of the processes of mitochondrial functions, mitochondrial electron transport and the generation of ATP and. EDS3: causative gene and the connection to dysautonomia and mastocytosis in my family. CrY2H-seq: a massively multiplexed assay for deep-coverage interactome mapping. Broad-scale protein–protein interaction mapping is a major challenge given the cost. Seq combines batch processing with a fully customizable reference sequence selection of organellar genome records from NCBI and/or references uploaded by the user. For the annotation of chloroplast genomes, the application additionally provides an integrated database of manually curated reference sequences. Ge. Seq identifies genes or other feature- encoding regions by BLAT- based homology searches and additionally, by profile HMM searches for protein and r. RNA coding genes and two de novo predictors for t. RNA genes. Reference. ARUP Laboratories is a national reference laboratory and a worldwide leader in innovative laboratory research and development. ARUP offers an extensive lab testing.Landscape and variation of novel retroduplications in 2. Retroduplications come from reverse transcription of m. RNAs and their insertion back into the genome. Here, we performed comprehensive discovery and analysis of retroduplications in a large cohort of 2,5. Genomes Phase 3. We developed an integrated approach to discover novel retroduplications combining high- coverage exome and low- coverage whole- genome sequencing data, utilizing information from both exon- exon junctions and discordant paired- end reads. We found 5. 03 parent genes having novel retroduplications absent from the reference genome. Based solely on retroduplication variation, we built phylogenetic trees of human populations; these represent superpopulation structure well and indicate that variable retroduplications are effective population markers. We further identified 4. This group contains several interesting insertion events, including a SLMO2 retroduplication and insertion into CAV3, which has a potential disease association.
Reference. Identification of long non- coding RNA in the horse transcriptome. Efforts to resolve the transcribed sequences in the equine genome have focused on protein- coding RNA. The transcription of the intergenic regions, although detected via total RNA sequencing (RNA- seq), has yet to be characterized in the horse. The most recent equine transcriptome based on RNA- seq from several tissues was a prime opportunity to obtain a concurrent long non- coding RNA (lnc. RNA) database. Utilizing the intergenic reads and three categories of novel genes from a previously published equine transcriptome pipeline, we better describe these groups by annotating the lnc. RNA candidates. These lnc. RNA candidates were filtered using an approach adapted from human lnc. RNA annotation, which removes transcripts based on size, expression, protein- coding capability and distance to the start or stop of annotated protein- coding transcripts. Ever since eukaryotes subsumed the bacterial ancestor of mitochondria, the nuclear and mitochondrial genomes have had to closely coordinate their activities, as. Reference. Mammographic density and ageing: A collaborative pooled analysis of cross- sectional data from 2. Mammographic density (MD) is one of the strongest breast cancer risk factors. Its age- related characteristics have been studied in women in western countries, but whether these associations apply to women worldwide is not known. We examined cross- sectional differences in MD by age and menopausal status in over 1. International Consortium on Mammographic Density (ICMD). MD was read centrally using a quantitative method (Cumulus) and its square- root metrics were analysed using meta- analysis of group- level estimates and linear regression models of pooled data, adjusted for body mass index, reproductive factors, mammogram view, image type, and reader. In all, 4,5. 34 women were premenopausal, and 6,4. A large age- adjusted difference in percent MD (PD) between post- and premenopausal women was apparent (–0. Among premenopausal women, the . Reference. Comparative genomics shows that viral integrations are abundant and express pi. RNAs in the arboviral vectors. Arthropod- borne viruses (arboviruses) transmitted by mosquito vectors cause many important emerging or resurging infectious diseases in humans including dengue, chikungunya and Zika. We used a bioinformatic approach to analyse the presence, abundance, distribution, and transcriptional activity of integrations from 4. Large differences in abundance and types of viral integrations were observed in mosquito species from the same region. Viral integrations are unexpectedly abundant in the arboviral vector species Aedes aegypti and Ae. Additionally, viral integrations are enriched in pi. RNA clusters of both the Ae. Reference. The genomic landscape of pediatric and young adult T- lineage acute lymphoblastic leukemia. Genetic alterations that activate NOTCH1 signaling and T cell transcription factors, coupled with inactivation of the INK4/ARF tumor suppressors, are hallmarks of T- lineage acute lymphoblastic leukemia (T- ALL), but detailed genome- wide sequencing of large T- ALL cohorts has not been carried out. Using integrated genomic analysis of 2. T- ALL cases, we identified 1. T- ALL (for example, CCND3, CTCF, MYB, SMARCA4, ZFP3. L2 and MYCN). We describe new mechanisms of coding and noncoding alteration and identify ten recurrently altered pathways, with associations between mutated genes and pathways, and stage or subtype of T- ALL. For example, NRAS/FLT3 mutations were associated with immature T- ALL, JAK3/STAT5. B mutations in HOXA1 deregulated ALL, PTPN2 mutations in TLX1 deregulated T- ALL, and PIK3. R1/PTEN mutations in TAL1 deregulated ALL, which suggests that different signaling pathways have distinct roles according to maturational stage. This genomic landscape provides a logical framework for the development of faithful genetic models and new therapeutic approaches. Reference. Fine- mapping inflammatory bowel disease loci to single- variant resolution. Inflammatory bowel diseases are chronic gastrointestinal inflammatory disorders that affect millions of people worldwide. Genome- wide association studies have identified 2. Here we report fine- mapping of 9. We pinpoint 1. 8 associations to a single causal variant with greater than 9. These 4. 5 variants are significantly enriched for protein- coding changes (n = 1. Crohn’s disease and in gut mucosa among associations stronger in ulcerative colitis. Reference. Pro. Hits- viz: a suite of web tools for visualizing interaction proteomics data. With the broad availability of mass spectrometry methods for identifying protein–protein interactions, there is a growing need for applications that can simplify the challenges associated with managing, analyzing and visualizing the results obtained by these methods. Here we present Pro. Hits- viz (prohits- viz. Reference. A comprehensive genomic pan- cancer classification using The Cancer Genome Atlas gene expression data. The Cancer Genome Atlas (TCGA) has generated comprehensive molecular profiles. We aim to identify a set of genes whose expression patterns can distinguish diverse tumor types. Those features may serve as biomarkers for tumor diagnosis and drug development. Accuracies were high for all but three of the 3. READ (rectum adenocarcinoma) which was largely indistinguishable from COAD (colon adenocarcinoma). We also carried out pan- cancer classification, separately for males and females, on 2. Results from these gender- specific analyses largely recapitulated results when gender was ignored. Remarkably, more than 8. Here we utilize a . Reference. De novo transcriptome analysis shows DEG in salivary glands of edible bird’s nest producing swiftlets. Edible bird’s nest (EBN), produced from solidified saliva secretions of specific swiftlet species during the breeding season, is one of the most valuable animal by- products in the world. The study described the transcriptomes of salivary glands from three swiftlet species (2. RNASeq. A total of 1. The current study investigated the genes and pathways that are associated with the development of salivary gland and EBN composition. Differential expression and pathway enrichment analysis indicated that the expression of CREB3. L2 and several signaling pathways involved in salivary gland development, namely, the EGFR, BMP, and MAPK signaling pathways, were up- regulated in swiftlets producing white EBN (Aerodramus fuciphagus) and black EBN (Aerodramus maximus) compared with non- EBN- producing swiftlets (Apus affinis). Furthermore, MGAT, an essential gene for the biosynthesis of N- acetylneuraminic acid (sialic acid), was highly expressed in both white- and black- nest swiftlets compared to non- EBN- producing swiftlets. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. In this work, we describe an algorithm to distinguish somatic single nucleotide variants (SNVs) in next- generation sequencing data from germline polymorphisms in the absence of normal samples using a machine learning approach. Our algorithm was evaluated using a family of supervised learning classifications across six different cancer types and ~1. Our algorithm correctly classified between 9. F1- measure ranges from 7. Here, we present a massively multiplexed yeast two- hybrid method, Cr. Y2. H- seq, which uses a Cre recombinase interaction reporter to intracellularly fuse the coding sequences of two interacting proteins and next- generation DNA sequencing to identify these interactions en masse.
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July 2017
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