Identifying the Genetic Explanation for Heritable Breast Cancer
Ontology highlight
ABSTRACT: The project developed a suite of new methods (FAVR) designed to assist the shortlisting of genetic variants under a rare variant-phenotype/disease model. The methods were designed to work with commonly used massively parallel sequencing analysis pipelines, such as the GATK or ANNOVAR, and have been made publically available as a suite of software tools (https://github.com/bjpop/favr). The FAVR methods use signatures in comparator sequence alignment files to facilitate the filtering of mapping artifacts and common genetic variants, and annotation of genetic variants based on evidence of co-occurrence in individuals. As relevant, FAVR methods can also be used to filter out artifacts derived from imbalanced paired-end sequencing. Pope et al., BMC Bioinformatics, accepted Dec 2012.
SECONDARY ACCESSION(S): PRJNA213029PRJNA213028
REPOSITORIES: dbGaP
Dataset's files
Items per page: 5 1 - 5 of 15 |
Similar Datasets
Project description:<p>The project developed a suite of new methods (FAVR) designed to assist the shortlisting of genetic variants under a rare variant-phenotype/disease model. The methods were designed to work with commonly used massively parallel sequencing analysis pipelines, such as the GATK or ANNOVAR, and have been made publically available as a suite of software tools (<a href="https://github.com/bjpop/favr" target="_blank">https://github.com/bjpop/favr</a>). The FAVR methods use signatures in comparator sequence alignment files to facilitate the filtering of mapping artifacts and common genetic variants, and annotation of genetic variants based on evidence of co-occurrence in individuals. As relevant, FAVR methods can also be used to filter out artifacts derived from imbalanced paired-end sequencing.</p> <p>Pope et al., BMC Bioinformatics, accepted Dec 2012.</p>
| phs000601.v1.p1 | EGA
Project description:<p>Schizophrenia (SCZ) is a devastating psychiatric disorder. The aim of this study was to detect DNA methylation (5meC) profiles in whole blood associated with SCZ. The sample consisted of schizophrenia cases and controls selected from national population registers in Sweden. We employed MBD protein-based enrichment of the methylated DNA fraction, followed by single end sequencing (50 bp reads) on the SOLiD platform. To improve methylome-wide coverage we used an existing protocol variant, which increases the relative number of fragments from CpG poor regions. To analyze the data, we used an analysis pipeline described elsewhere7, that was designed specifically for MBD-seq and included alignment, quality control (QC) of reads and samples, coverage calculations for the 26,752,702 autosomal CpGs in the reference genome using a non-parametric estimate of the fragment size distribution, an in silico experiment to identify CpGs in loci with alignment problems, a 2-stage adaptive algorithm to combine data from correlated adjacent CpG sites in "blocks", principal component analysis (PCA), association testing, bioinformatics annotation and network analysis. After QC, 1459 subjects with on average 32.4 million high quality reads remained. Association testing was performed on 4.3 million high quality blocks after regressing out possible assay related technical artifacts, sex/age, and unmeasured confounders through the PCA components.</p>
| phs000608 | dbGaP
Project description:Data from multiple high throughput technologies such as RNA sequencing (RNA-Seq) and protein mass spectrometry (MS/MS) are often used to assist in predicting eukaryote genome features such as genes, splice variants, and single nucleotide variants (SNVs). The genomes of parasitic nematodes causing neglected tropical diseases are often poorly annotated. Angiostrongylus costaricensis, a nematode that causes an intestinal inflammatory disease known as abdominal angiostrongyliasis (AA), is one example. Currently, no drugs or treatments are available for AA, a public health problem in Latin America, especially in Costa Rica and Brazil. The available genome of A. costaricensis, specific to the Costa Rica strain, is a draft version not supported by transcript- or protein-level evidence. This study used RNA-Seq and MS/MS data to perform an in-depth annotation of the A. costaricensis genome. Our prediction supplemented the reference annotation with a) novel coding and non-coding genes; b) pieces of evidence of alternative splicing generating new proteoforms; c) a list of SNVs specific to the Brazilian strain (Crissiumal). To the best of our knowledge, this is the first time that a multi-omics approach has been used to improve the genome annotation of a parasitic nematode. We hope this supplemented genome annotation can assist the future development of drugs to treat AA caused by either Brazil strain (Crissiumal) or Costa Rica strain.
2023-03-11 | PXD034605 | Pride
Project description:Enhanced cross-linking immunoprecipitation (eCLIP) featuring a size-matched input control has been recently applied to profile the binding sites of more than one hundred RNA binding proteins (RBPs). However computational pipelines and quality control metrics needed to process CLIP data at scale have yet to be well defined. Here, we describe our ENCODE eCLIP processing pipeline (https://github.com/YeoLab/eclip), enabling users to go from raw reads to processed peaks that are enriched above paired input, reproducible across biological replicates, and can be directly compared against the public ENCODE eCLIP resource. In particular, we discuss processing steps designed to address common artifacts, including properly quantifying unique RNA fragments bound by both unique genomic- and repetitive element-mapped reads. Using manual quality annotation of 350 ENCODE eCLIP experiments, we develop metrics for quality assessment of eCLIP experiments prior to and after sequencing, including library yield, number of unique fragments in the library, total binding relative information, and biological reproducibility. In particular, we quantify the commonly believed linkage between depth of sequencing and peak discovery, and derive methods for estimating required sequencing depth based on pre-sequencing metrics. Finally we provide recommendations for the common question of integrating RBP binding information with RNA-seq to generate splicing maps representing the positional effect of binding on alternative splicing. These pipelines and QC metrics enable large-scale processing and analysis of eCLIP data, and will help to standardize rigorous analysis of RBP binding data.
2018-06-08 | GSE107767 | GEO
Project description:Enhanced cross-linking immunoprecipitation (eCLIP) featuring a size-matched input control has been recently applied to profile the binding sites of more than one hundred RNA binding proteins (RBPs). However computational pipelines and quality control metrics needed to process CLIP data at scale have yet to be well defined. Here, we describe our ENCODE eCLIP processing pipeline (https://github.com/YeoLab/eclip), enabling users to go from raw reads to processed peaks that are enriched above paired input, reproducible across biological replicates, and can be directly compared against the public ENCODE eCLIP resource. In particular, we discuss processing steps designed to address common artifacts, including properly quantifying unique RNA fragments bound by both unique genomic- and repetitive element-mapped reads. Using manual quality annotation of 350 ENCODE eCLIP experiments, we develop metrics for quality assessment of eCLIP experiments prior to and after sequencing, including library yield, number of unique fragments in the library, total binding relative information, and biological reproducibility. In particular, we quantify the commonly believed linkage between depth of sequencing and peak discovery, and derive methods for estimating required sequencing depth based on pre-sequencing metrics. Finally we provide recommendations for the common question of integrating RBP binding information with RNA-seq to generate splicing maps representing the positional effect of binding on alternative splicing. These pipelines and QC metrics enable large-scale processing and analysis of eCLIP data, and will help to standardize rigorous analysis of RBP binding data.
2018-06-08 | GSE107766 | GEO
Project description:Motivation: Sample source, procurement process, and other technical variations introduce batch effects into genomics data. Algorithms to remove these artifacts enhance differences between known biological covariates, but also carry potential concern of removing intra-group biological heterogeneity and thus any personalized genomic signatures. As a result, accurate identification of novel subtypes from batch corrected genomics data is challenging using standard algorithms designed to remove batch effects for class comparison analyses. Nor can batch effects be corrected reliably in future applications of genomics-based clinical tests, in which the biological groups are by definition unknown a priori. Results: Therefore, we introduce new algorithm, personalized-SVA (pSVA), blind to biological covariates corrected technical artifacts while retaining biological heterogeneity in genomic data. This algorithm facilitated accurate subtype identification in head and neck cancer from gene expression data in both formalin fixed and frozen samples. When applied to predict HPV status, pSVA improved cross- study validation even if the sample batches were highly confounded with HPV status in the training set. Availability: All analyses were performed using R version 2.15.0. The code and data used to generate the results of this manuscript is available from https://sourceforge.net/projects/psva.
2016-12-12 | GSE53355 | GEO
Project description:This dataset was utilized to assess the performance of a novel de novo metaproteomics pipeline, which performs sequence alignment of de novo sequences from complete metaproteomics experiments. Traditionally, metaproteomics data annotation relies on database searching that requires sample-specific databases derived from whole metagenome sequencing experiments. Creating these databases, however, is a complex, time-consuming, and error prone process, which can introduce biases affecting the outcomes and conclusions, highlighting the need for alternative methods. The evaluated approach offers rapid and orthogonal insights into metaproteomics data.
2024-10-10 | PXD050548 | Pride
Project description:RNA-Seq provides an effective way to annotate and measure the transcriptome, but the combination of cost, analysis and end-mapping challenges has limited its adoption for high-throughput quantitation and annotation. Here, we present an integrated experimental and computational suite for transcriptome annotation and quantitation based on the sequencing of mRNA ends. It consists of: a novel, simple, one-step, strategy 5' RNA-Seq; an optimized library construction method for strand-specific 3' RNA-seq that reduces costs and time; and a complete computational analysis toolkit. We demonstrate the power and versatility of our approach to study the transcriptome of LPS stimulation in mouse dendritic cells, promoter structure of the TCRb locus in mouse T cells, and gene expression in circadian cycles in Drosophila. Our platform provides a comprehensive solution for high-throughput, cost-effective transcriptome quantification and end annotation. A study of 5' and 3' end RNA sequencing methods
2013-06-01 | E-GEOD-47207 | biostudies-arrayexpress
Project description:Chromatin accessibility captures the binding status of protein factors to chromosomes in vivo, and has been considered a highly informative proxy for functional protein-DNA interactions. Existing DNase I and Tn5 transposase based assays generally require tens of thousands to millions of fresh cells. Applying Tn5 tagmentation to single cells yields very sparse maps. Here we present a transposome hypersensitive sites sequencing assay (THS-seq) for highly sensitive characterizations of chromatin accessibility. Validation of THS-seq method, and comparison of DNase-seq, ATAC-seq and THS-seq methods for quantitation of chromatin accessibility. GSE47753, GSM1155957 GM12878_ATACseq_50k_Rep1, data downsampled to 8,351,125 reads for analysis: pub_SRR891268_ATAC-seq_50k_cells_Rep1_downsampled_dfilter_peaks.bed.gz GSE47753, GSM1155958 GM12878_ATACseq_50k_Rep2, data downsampled to 8,351,125 reads for analysis: pub_SRR891269_ATAC-seq_50k_cells_Rep2_downsampled_dfilter_peaks.bed.gz GSE47753, GSM1155959 GM12878_ATACseq_50k_Rep3, data downsampled to 8,351,125 reads for analysis: pub_SRR891270_ATAC-seq_50k_cells_Rep3_downsampled_dfilter_peaks.bed.gz GSE47753, GSM1155960 GM12878_ATACseq_50k_Rep4, data downsampled to 8,351,125 reads for analysis: pub_SRR891271_ATAC-seq_50k_cells_Rep4_downsampled_dfilter_peaks.bed.gz GSE47753, GSM1155961 GM12878_ATACseq_500_Rep1, data downsampled to 8,351,125 reads for analysis: pub_SRR891272_ATAC-seq_500_cells_Rep1_downsampled_dfilter_peaks.bed.gz GSE47753, GSM1155962 GM12878_ATACseq_500_Rep2, data downsampled to 8,351,125 reads for analysis: pub_SRR891273_ATAC-seq_500_cells_Rep2_downsampled_dfilter_peaks.bed.gz raw data files were merged, alignment with bowtie 1.3 using parameters, bowtie -n1 -k1 --best --chunkmbs 10240 --strata -l32 -m1 -p4 --nomaqround --sam, followed by clonal read removal for the final data file for further analysis. GSM816665, raw data obtained from UCSC genome browser, https://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeOpenChromDnase: wgEncodeOpenChromDnaseGm12878RawData_merged_unique_dfilter_peaks.bed.gz raw data files were merged, alignment with bowtie 1.3 using parameters, bowtie -n1 -k1 --best --chunkmbs 10240 --strata -l32 -m1 -p4 --nomaqround --sam, followed by clonal read removal for the final data file for further analysis. GSM864360, raw data obtained from UCSC genome browser, https://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeOpenChromFaire: wgEncodeOpenChromFaireGm12878RawData_merged_unique_dfilter_peaks.bed.gz raw data files were merged, alignment with bowtie 1.3 using parameters, bowtie -n1 -k1 --best --chunkmbs 10240 --strata -l32 -m1 -p4 --nomaqround --sam, followed by clonal read removal for the final data file for further analysis. GSM736496, GSM736620, raw data obtained from UCSC genome browser, https://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeUwDnase: wgEncodeUwDnaseGm12878RawData_merged_unique_dfilter_peaks.bed.gz
2016-01-09 | E-GEOD-72089 | biostudies-arrayexpress
Project description:Background: Parkinson’s disease (PD) is a multisystem and multifactorial disorder. Therefore, the application of modern genetic techniques may assist in unraveling its complex pathophysiology. Methods: We conducted a clinical-demographic evaluation of 126 patients with PD, all of whom were Caucasian and of Sicilian ancestry. DNA was extracted from peripheral blood for each patient, followed by sequencing using a Next-Generation Sequencing system. This system was based on a custom gene panel comprising 162 genes. The sample underwent further filtering, taking into account allele frequencies of genetic variants, their presence in the Human Gene Mutation Database, and their association in the literature with PD or other movement/neurodegenerative disorders. Results: The largest number of variants was identified in the leucine-rich repeat kinase 2 (LRRK2) gene. However, variants in other genes, such as acid beta-glucosidase (GBA), DNA polymerase gamma, catalytic subunit (POLG), and parkin RBR E3 ubiquitin protein ligase (PRKN), were also discovered. Interestingly, some of these variants had not been previously associated with PD. Conclusions: Enhancing our understanding of the genetic basis of PD and identifying new variants possibly linked to the disease will contribute to improved diagnostic accuracy, therapeutic developments, and prognostic insights for affected individuals.
2024-10-14 | E-MTAB-13523 | biostudies-arrayexpress