Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Clonify: unseeded antibody lineage assignment from next-generation sequencing data.

ABSTRACT: Defining the dynamics and maturation processes of antibody clonal lineages is crucial to understanding the humoral response to infection and immunization. Although individual antibody lineages have been previously analyzed in isolation, these studies provide only a narrow view of the total antibody response. Comprehensive study of antibody lineages has been limited by the lack of an accurate clonal lineage assignment algorithm capable of operating on next-generation sequencing datasets. To address this shortcoming, we developed Clonify, which is able to perform unseeded lineage assignment on very large sets of antibody sequences. Application of Clonify to IgG+ memory repertoires from healthy individuals revealed a surprising lack of influence of large extended lineages on the overall repertoire composition, indicating that this composition is driven less by the order and frequency of pathogen encounters than previously thought. Clonify is freely available at www.github.com/briney/clonify-python.

SUBMITTER: Briney B

PROVIDER: S-EPMC4840318 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Clonify: unseeded antibody lineage assignment from next-generation sequencing data.

Briney Bryan B Le Khoa K Zhu Jiang J Burton Dennis R DR

Scientific reports 20160422

Defining the dynamics and maturation processes of antibody clonal lineages is crucial to understanding the humoral response to infection and immunization. Although individual antibody lineages have been previously analyzed in isolation, these studies provide only a narrow view of the total antibody response. Comprehensive study of antibody lineages has been limited by the lack of an accurate clonal lineage assignment algorithm capable of operating on next-generation sequencing datasets. To addre ...[more]

PMID: 27102563

Similar Datasets

NGSNGS: next-generation simulator for next-generation sequencing data.

Project description:SummaryWith the rapid expansion of the capabilities of the DNA sequencers throughout the different sequencing generations, the quantity of generated data has likewise increased. This evolution has also led to new bioinformatical methods, for which in silico data have become crucial when verifying the accuracy of a model or the robustness of a genomic analysis pipeline. Here, we present a multithreaded next-generation simulator for next-generation sequencing data (NGSNGS), which simulates reads faster than currently available methods and programs. NGSNGS can simulate reads with platform-specific characteristics based on nucleotide quality score profiles as well as including a post-mortem damage model which is relevant for simulating ancient DNA. The simulated sequences are sampled (with replacement) from a reference DNA genome, which can represent a haploid genome, polyploid assemblies or even population haplotypes and allows the user to simulate known variable sites directly. The program is implemented in a multithreading framework and is factors faster than currently available tools while extending their feature set and possible output formats.Availability and implementationThe method and associated programs are released as open-source software, code and user manual are available at https://github.com/RAHenriksen/NGSNGS.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC9891242 | biostudies-literature

Next Generation Protein Sequencing

Project description:NGPS is a method for de-novo, full-length protein sequencing in high throughput. The method is based on cleavage of the protein at semi-random sites by microwave-assisted acid hydrolysis (MAAH), enrichment of LC-MS/MS amenable peptides from the hydrolysate by solid-phase-extraction, LC-MS/MS analysis, de-novo long peptide tag sequencing of resulting peptides and assembly of peptide tags into consensus contigs.

2017-04-03 | PXD003804 | Pride

Filtering Next-Generation Sequencing of the Ig Gene Repertoire Data Using Antibody Structural Information.

Project description:Next-generation sequencing of the Ig gene repertoire (Ig-seq) produces large volumes of information at the nucleotide sequence level. Such data have improved our understanding of immune systems across numerous species and have already been successfully applied in vaccine development and drug discovery. However, the high-throughput nature of Ig-seq means that it is afflicted by high error rates. This has led to the development of error-correction approaches. Computational error-correction methods use sequence information alone, primarily designating sequences as likely to be correct if they are observed frequently. In this work, we describe an orthogonal method for filtering Ig-seq data, which considers the structural viability of each sequence. A typical natural Ab structure requires the presence of a disulfide bridge within each of its variable chains to maintain the fold. Our Ab Sequence Selector (ABOSS) uses the presence/absence of this bridge as a way of both identifying structurally viable sequences and estimating the sequencing error rate. On simulated Ig-seq datasets, ABOSS is able to identify more than 99% of structurally viable sequences. Applying our method to six independent Ig-seq datasets (one mouse and five human), we show that our error calculations are in line with previous experimental and computational error estimates. We also show how ABOSS is able to identify structurally impossible sequences missed by other error-correction methods.

| S-EPMC6485405 | biostudies-literature

Pathway analysis with next-generation sequencing data.

Project description:Although pathway analysis methods have been developed and successfully applied to association studies of common variants, the statistical methods for pathway-based association analysis of rare variants have not been well developed. Many investigators observed highly inflated false-positive rates and low power in pathway-based tests of association of rare variants. The inflated false-positive rates and low true-positive rates of the current methods are mainly due to their lack of ability to account for gametic phase disequilibrium. To overcome these serious limitations, we develop a novel statistic that is based on the smoothed functional principal component analysis (SFPCA) for pathway association tests with next-generation sequencing data. The developed statistic has the ability to capture position-level variant information and account for gametic phase disequilibrium. By intensive simulations, we demonstrate that the SFPCA-based statistic for testing pathway association with either rare or common or both rare and common variants has the correct type 1 error rates. Also the power of the SFPCA-based statistic and 22 additional existing statistics are evaluated. We found that the SFPCA-based statistic has a much higher power than other existing statistics in all the scenarios considered. To further evaluate its performance, the SFPCA-based statistic is applied to pathway analysis of exome sequencing data in the early-onset myocardial infarction (EOMI) project. We identify three pathways significantly associated with EOMI after the Bonferroni correction. In addition, our preliminary results show that the SFPCA-based statistic has much smaller P-values to identify pathway association than other existing methods.

| S-EPMC4666565 | biostudies-literature

Next-Generation Anchor Based Phylogeny (NexABP): constructing phylogeny from next-generation sequencing data.

Project description:Whole genome sequences are ideally suited for deriving evolutionary relationship among organisms. With the availability of Next Generation sequencing (NGS) datasets in an unprecedented scale, it will be highly desirable if phylogenetic analysis can be carried out using short read NGS data. We described here an anchor based approach NexABP for phylogenetic construction of closely related strains/isolates from NGS data. This approach can be used even in the absence of a fully assembled reference genome and works by reducing the complexity of the datasets without compromising results. NexABP was used for constructing phylogeny of different strains of some of the common pathogens, such as Mycobacterium tuberculosis, Vibrio cholera and Escherichia coli. In addition to classification into distinct lineages, NexABP could resolve inner branches and also allow statistical testing using bootstrap analysis. We believe that there are some clear advantages of using NexABP based phylogenetic analysis as compared to other methods.

| S-EPMC3769656 | biostudies-literature

Estimating individual admixture proportions from next generation sequencing data.

Project description:Inference of population structure and individual ancestry is important both for population genetics and for association studies. With next generation sequencing technologies it is possible to obtain genetic data for all accessible genetic variations in the genome. Existing methods for admixture analysis rely on known genotypes. However, individual genotypes cannot be inferred from low-depth sequencing data without introducing errors. This article presents a new method for inferring an individual's ancestry that takes the uncertainty introduced in next generation sequencing data into account. This is achieved by working directly with genotype likelihoods that contain all relevant information of the unobserved genotypes. Using simulations as well as publicly available sequencing data, we demonstrate that the presented method has great accuracy even for very low-depth data. At the same time, we demonstrate that applying existing methods to genotypes called from the same data can introduce severe biases. The presented method is implemented in the NGSadmix software available at http://www.popgen.dk/software.

| S-EPMC3813857 | biostudies-other

Quantifying population genetic differentiation from next-generation sequencing data.

Project description:Over the past few years, new high-throughput DNA sequencing technologies have dramatically increased speed and reduced sequencing costs. However, the use of these sequencing technologies is often challenged by errors and biases associated with the bioinformatical methods used for analyzing the data. In particular, the use of naïve methods to identify polymorphic sites and infer genotypes can inflate downstream analyses. Recently, explicit modeling of genotype probability distributions has been proposed as a method for taking genotype call uncertainty into account. Based on this idea, we propose a novel method for quantifying population genetic differentiation from next-generation sequencing data. In addition, we present a strategy for investigating population structure via principal components analysis. Through extensive simulations, we compare the new method herein proposed to approaches based on genotype calling and demonstrate a marked improvement in estimation accuracy for a wide range of conditions. We apply the method to a large-scale genomic data set of domesticated and wild silkworms sequenced at low coverage. We find that we can infer the fine-scale genetic structure of the sampled individuals, suggesting that employing this new method is useful for investigating the genetic relationships of populations sampled at low coverage.

| S-EPMC3813878 | biostudies-other

Identification of indels in next-generation sequencing data.

Project description:BackgroundThe discovery and mapping of genomic variants is an essential step in most analysis done using sequencing reads. There are a number of mature software packages and associated pipelines that can identify single nucleotide polymorphisms (SNPs) with a high degree of concordance. However, the same cannot be said for tools that are used to identify the other types of variants. Indels represent the second most frequent class of variants in the human genome, after single nucleotide polymorphisms. The reliable detection of indels is still a challenging problem, especially for variants that are longer than a few bases.ResultsWe have developed a set of algorithms and heuristics collectively called indelMINER to identify indels from whole genome resequencing datasets using paired-end reads. indelMINER uses a split-read approach to identify the precise breakpoints for indels of size less than a user specified threshold, and supplements that with a paired-end approach to identify larger variants that are frequently missed with the split-read approach. We use simulated and real datasets to show that an implementation of the algorithm performs favorably when compared to several existing tools.ConclusionsindelMINER can be used effectively to identify indels in whole-genome resequencing projects. The output is provided in the VCF format along with additional information about the variant, including information about its presence or absence in another sample. The source code and documentation for indelMINER can be freely downloaded from www.bx.psu.edu/miller_lab/indelMINER.tar.gz .

| S-EPMC4339746 | biostudies-literature

SAMStat: monitoring biases in next generation sequencing data.

Project description:MotivationThe sequence alignment/map format (SAM) is a commonly used format to store the alignments between millions of short reads and a reference genome. Often certain positions within the reads are inherently more likely to contain errors due to the protocols used to prepare the samples. Such biases can have adverse effects on both mapping rate and accuracy. To understand the relationship between potential protocol biases and poor mapping we wrote SAMstat, a simple C program plotting nucleotide overrepresentation and other statistics in mapped and unmapped reads in a concise html page. Collecting such statistics also makes it easy to highlight problems in the data processing and enables non-experts to track data quality over time.ResultsWe demonstrate that studying sequence features in mapped data can be used to identify biases particular to one sequencing protocol. Once identified, such biases can be considered in the downstream analysis or even be removed by read trimming or filtering techniques.AvailabilitySAMStat is open source and freely available as a C program running on all Unix-compatible platforms. The source code is available from http://samstat.sourceforge.net.Contacttimolassmann@gmail.com.

| S-EPMC3008642 | biostudies-literature

The antibody genetics of multiple sclerosis: comparing next-generation sequencing to sanger sequencing.

Project description:We previously identified a distinct mutation pattern in the antibody genes of B cells isolated from cerebrospinal fluid (CSF) that can identify patients who have relapsing-remitting multiple sclerosis (RRMS) and patients with clinically isolated syndromes who will convert to RRMS. This antibody gene signature (AGS) was developed using Sanger sequencing of single B cells. While potentially helpful to patients, Sanger sequencing is not an assay that can be practically deployed in clinical settings. In order to provide AGS evaluations to patients as part of their diagnostic workup, we developed protocols to generate AGS scores using next-generation DNA sequencing (NGS) on CSF-derived cell pellets without the need to isolate single cells. This approach has the potential to increase the coverage of the B-cell population being analyzed, reduce the time needed to generate AGS scores, and may improve the overall performance of the AGS approach as a diagnostic test in the future. However, no investigations have focused on whether NGS-based repertoires will properly reflect antibody gene frequencies and somatic hypermutation patterns defined by Sanger sequencing. To address this issue, we isolated paired CSF samples from eight patients who either had MS or were at risk to develop MS. Here, we present data that antibody gene frequencies and somatic hypermutation patterns are similar in Sanger and NGS-based antibody repertoires from these paired CSF samples. In addition, AGS scores derived from the NGS database correctly identified the patients who initially had or subsequently converted to RRMS, with precision similar to that of the Sanger sequencing approach. Further investigation of the utility of the AGS in predicting conversion to MS using NGS-derived antibody repertoires in a larger cohort of patients is warranted.

| S-EPMC4165282 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data