A comprehensive and scalable database search system for metaproteomics

AbstractBackgroundMass spectrometry-based shotgun proteomics experiments rely on accurate matching of experimental spectra against a database of protein sequences. Existing computational analysis methods are limited in the size of their sequence databases, which severely restricts the proteomic sequencing depth and functional analysis of highly complex samples. The growing amount of public high-throughput sequencing data will only exacerbate this problem. We designed a broadly applicable metaproteomic analysis method (ComPIL) that addresses protein database size limitations.ResultsOur approach to overcome this significant limitation in metaproteomics was to design a scalable set of sequence databases assembled for optimal library querying speeds. ComPIL was integrated with a modified version of the search engine ProLuCID (termed “Blazmass”) to permit rapid matching of experimental spectra. Proof-of-principle analysis of human HEK293 lysate with a ComPIL database derived from high-quality genomic libraries was able to detect nearly all of the same peptides as a search with a human database (~500x fewer peptides in the database), with a small reduction in sensitivity. We were also able to detect proteins from the adenovirus used to immortalize these cells. We applied our method to a set of healthy human gut microbiome proteomic samples and showed a substantial increase in the number of identified peptides and proteins compared to previous metaproteomic analyses, while retaining a high degree of protein identification accuracy, and allowing for a more in-depth characterization of the functional landscape of the samples.ConclusionsThe combination of ComPIL with Blazmass allows proteomic searches to be performed with database sizes much larger than previously possible. These large database searches can be applied to complex meta-samples with unknown composition or proteomic samples where unexpected proteins may be identified. The protein database, proteomics search engine, and the proteomic data files for the 5 microbiome samples characterized and discussed herein are open source and available for use and additional analysis.</jats:sec

Narrow Versus Wide Tuning Curves: What’s Best for a Population Code?

Neurophysiologists are often faced with the problem of evaluating the quality of a code for a sensory or motor variable, either to relate it to the performance of the animal in a simple discrimination task or to compare the codes at various stages along the neuronal pathway. One common belief that has emerged from such studies is that sharpening of tuning curves improves the quality of the code, although only to a certain point; sharpening beyond that is believed to be harmful. We show that this belief relies on either problematic technical analysis or improper assumptions about the noise. We conclude that one cannot tell, in the general case, whether narrow tuning curves are better than wide ones; the answer depends critically on the covariance of the noise. The same conclusion applies to other manipulations of the tuning curve profiles such as gain increase

Multisensory spatial representations in eye-centered coordinates for reaching

Humans can reach for objects with their hands whether the objects are seen, heard or touched. Thus, the position of objects is recoded in a joint-centered frame of reference regardless of the sensory modality involved. Our study indicates that this frame of reference is not the only one shared across sensory modalities. The location of reaching targets is also encoded in eye-centered coordinates, whether the targets are visual, auditory, proprioceptive or imaginary. Furthermore, the remembered eye-centered location is updated after each eye and head movement. This is quite surprising since, in principle, a reaching motor command can be computed from any non-visual modality without ever recovering the eye-centered location of the stimulus. This finding may reflect the predominant role of vision in human spatial perception

Narrow Vs Wide Tuning Curves: What&apos;s Best for a Population Code?

Neurophysiologists are often faced with the problem of evaluating the quality of a code for a sensory or motor variable, either to relate it to the performance of the animal in a simple discrimination task, or to compare the codes at various stages along the neuronal pathway. One common belief that has emerged from such studies is that sharpening of tuning curves improves the quality of the code, although only to a certain point beyond which further sharpening is believed to be harmful. We show that this belief relies on either problematic technical analysis or improper assumptions about the noise. We conclude that one cannot tell, in the general case, whether narrow tuning curves are better than wide ones; the answer depends critically on the covariance of the noise. The same conclusion applies to other manipulations of the tuning curve profiles such as gain increase

Omics Pipe: A Computational Framework for Reproducible Multi-Omics Data Analysis

Omics Pipe (https://bitbucket.org/sulab/omics_pipe) is a computational platform that automates multi-omics data analysis pipelines on high performance compute clusters and in the cloud. It supports best practice published pipelines for RNA-seq, miRNA-seq, Exome-seq, Whole Genome sequencing, ChIP-seq analyses and automatic processing of data from The Cancer Genome Atlas. Omics Pipe provides researchers with a tool for reproducible, open source and extensible next generation sequencing analysis.</jats:p