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An automated Bayesian pipeline for rapid analysis of single-molecule binding data.

ABSTRACT: Single-molecule binding assays enable the study of how molecular machines assemble and function. Current algorithms can identify and locate individual molecules, but require tedious manual validation of each spot. Moreover, no solution for high-throughput analysis of single-molecule binding data exists. Here, we describe an automated pipeline to analyze single-molecule data over a wide range of experimental conditions. In addition, our method enables state estimation on multivariate Gaussian signals. We validate our approach using simulated data, and benchmark the pipeline by measuring the binding properties of the well-studied, DNA-guided DNA endonuclease, TtAgo, an Argonaute protein from the Eubacterium Thermus thermophilus. We also use the pipeline to extend our understanding of TtAgo by measuring the protein's binding kinetics at physiological temperatures and for target DNAs containing multiple, adjacent binding sites.

SUBMITTER: Smith CS

PROVIDER: S-EPMC6336789 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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An automated Bayesian pipeline for rapid analysis of single-molecule binding data.

Smith Carlas S CS Jouravleva Karina K Huisman Maximiliaan M Jolly Samson M SM Zamore Phillip D PD Grunwald David D

Nature communications 20190117 1

Single-molecule binding assays enable the study of how molecular machines assemble and function. Current algorithms can identify and locate individual molecules, but require tedious manual validation of each spot. Moreover, no solution for high-throughput analysis of single-molecule binding data exists. Here, we describe an automated pipeline to analyze single-molecule data over a wide range of experimental conditions. In addition, our method enables state estimation on multivariate Gaussian sig ...[more]

PMID: 30655518

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Project description:BACKGROUNDResearch involving microbial ecosystems has drawn increasing attention in recent years. Studying microbe-microbe, host-microbe, and environment-microbe interactions are essential for the understanding of microbial ecosystems. Currently, metaproteomics provide qualitative and quantitative information of proteins, providing insights into the functional changes of microbial communities. However, computational analysis of large-scale data generated in metaproteomic studies remains a challenge. Conventional proteomic software have difficulties dealing with the extreme complexity and species diversity present in microbiome samples leading to lower rates of peptide and protein identification. To address this issue, we previously developed the MetaPro-IQ approach for highly efficient microbial protein/peptide identification and quantification.RESULTHere, we developed an integrated software platform, named MetaLab, providing a complete and automated, user-friendly pipeline for fast microbial protein identification, quantification, as well as taxonomic profiling, directly from mass spectrometry raw data. Spectral clustering adopted in the pre-processing step dramatically improved the speed of peptide identification from database searches. Quantitative information of identified peptides was used for estimating the relative abundance of taxa at all phylogenetic ranks. Taxonomy result files exported by MetaLab are fully compatible with widely used metagenomics tools. Herein, the potential of MetaLab is evaluated by reanalyzing a metaproteomic dataset from mouse gut microbiome samples.CONCLUSIONMetaLab is a fully automatic software platform enabling an integrated data-processing pipeline for metaproteomics. The function of sample-specific database generation can be very advantageous for searching peptides against huge protein databases. It provides a seamless connection between peptide determination and taxonomic profiling; therefore, the peptide abundance is readily used for measuring the microbial variations. MetaLab is designed as a versatile, efficient, and easy-to-use tool which can greatly simplify the procedure of metaproteomic data analysis for researchers in microbiome studies.

Project description:MotivationThe advent of Next Generation Sequencing (NGS) has led to the generation of enormous volumes of short read sequence data, cheaply and in reasonable time scales. Nevertheless, the quality of genome assemblies generated using NGS technologies has been greatly affected, compared to those generated using Sanger DNA sequencing. This is largely due to the inability of short read sequence data to scaffold repetitive structures, creating gaps, inversions and rearrangements and resulting in assemblies that are, at best, draft forms. Third generation single-molecule sequencing (SMS) technologies (e.g. Pacific Biosciences Single Molecule Real Time (SMRT) system) address this challenge by generating sequences with increased read lengths, offering the prospect to better recover these complex repetitive structures, concomitantly improving assembly quality.ResultsHere, we evaluate the ability of SMS data (specifically human genome Pacific Biosciences SMRT data) to recover poorly represented repetitive sequences (specifically, GC-rich human minisatellites). To do this we designed a pipeline for the collection, processing and local assembly of single-molecule sequence data to form accurate contiguous local reconstructions. Our results show the recovery of an allele of the non-coding minisatellite MS1 (located on chromosome 1 at 1p33-35) at greater than 97% identity to reference (GRCh38) from the unprocessed sequence data of a haploid complete hydatidiform mole (CHM1) cell line. Furthermore, our assembly revealed an allele of over 500 repeat units; much larger than the reference (GRCh38), but consistent in structure with naturally occurring alleles that are segregating in human populations. This local assembly's reconstruction was validated with the release of the whole genome assemblies GCA_001297185.1 and GCA_000772585.3, where this allele occurs. Additionally, application of this pipeline to coding minisatellites in the PRDM9 and ZNF93 genes enabled recovery of high identity allele structures for these sequence regions whose length was confirmed by PCR from cell line genomic DNA. The internal repeat structure of the PRDM9 allele recovered was consistent with common human-specific alleles.Availability and implementationCode available at https://github.com/ndliberial/smrt_pipeline.Contactdno2@le.ac.uk.

Dataset Information

An automated Bayesian pipeline for rapid analysis of single-molecule binding data.

Publications

An automated Bayesian pipeline for rapid analysis of single-molecule binding data.

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