scholarly journals KIMI: Knockoff Inference for Motif Identification from molecular sequences with controlled false discovery rate

2020 ◽  
Author(s):  
Xin Bai ◽  
Jie Ren ◽  
Yingying Fan ◽  
Fengzhu Sun
Keyword(s):  
False Discovery Rate ◽  
Motif Discovery ◽  
Rapid Development ◽  
Biological Significance ◽  
Supplementary Information ◽  
Sequence Motif ◽  
Metagenomic Sequencing ◽  
Simulation Studies ◽  
Sequencing Technologies ◽  
False Discovery

Abstract Motivation The rapid development of sequencing technologies has enabled us to generate a large number of metagenomic reads from genetic materials in microbial communities, making it possible to gain deep insights into understanding the differences between the genetic materials of different groups of microorganisms, such as bacteria, viruses, plasmids, etc. Computational methods based on k-mer frequencies have been shown to be highly effective for classifying metagenomic sequencing reads into different groups. However, such methods usually use all the k-mers as features for prediction without selecting relevant k-mers for the different groups of sequences, i.e. unique nucleotide patterns containing biological significance. Results To select k-mers for distinguishing different groups of sequences with guaranteed false discovery rate (FDR) control, we develop KIMI, a general framework based on model-X Knockoffs regarded as the state-of-the-art statistical method for FDR control, for sequence motif discovery with arbitrary target FDR level, such that reproducibility can be theoretically guaranteed. KIMI is shown through simulation studies to be effective in simultaneously controlling FDR and yielding high power, outperforming the broadly used Benjamini–Hochberg procedure and the q-value method for FDR control. To illustrate the usefulness of KIMI in analyzing real datasets, we take the viral motif discovery problem as an example and implement KIMI on a real dataset consisting of viral and bacterial contigs. We show that the accuracy of predicting viral and bacterial contigs can be increased by training the prediction model only on relevant k-mers selected by KIMI. Availabilityand implementation Our implementation of KIMI is available at https://github.com/xinbaiusc/KIMI. Supplementary information Supplementary data are available at Bioinformatics online.

DiffNetFDR: differential network analysis with false discovery rate control

2019 ◽  
Vol 35 (17) ◽  
pp. 3184-3186
Author(s):  
Xiao-Fei Zhang ◽  
Le Ou-Yang ◽  
Shuo Yang ◽  
Xiaohua Hu ◽  
Hong Yan
Keyword(s):  
False Discovery Rate ◽  
Graphical Models ◽  
Biological Significance ◽  
R Package ◽  
Supplementary Information ◽  
Gaussian Graphical Models ◽  
Multiple Testing Procedure ◽  
False Discovery ◽  
Differential Network ◽  
Shiny App

Abstract Summary To identify biological network rewiring under different conditions, we develop a user-friendly R package, named DiffNetFDR, to implement two methods developed for testing the difference in different Gaussian graphical models. Compared to existing tools, our methods have the following features: (i) they are based on Gaussian graphical models which can capture the changes of conditional dependencies; (ii) they determine the tuning parameters in a data-driven manner; (iii) they take a multiple testing procedure to control the overall false discovery rate; and (iv) our approach defines the differential network based on partial correlation coefficients so that the spurious differential edges caused by the variants of conditional variances can be excluded. We also develop a Shiny application to provide easier analysis and visualization. Simulation studies are conducted to evaluate the performance of our methods. We also apply our methods to two real gene expression datasets. The effectiveness of our methods is validated by the biological significance of the identified differential networks. Availability and implementation R package and Shiny app are available at https://github.com/Zhangxf-ccnu/DiffNetFDR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ChIPWig: A Random Access-Enabling Lossless and Lossy Compression Method for ChIP-seq Data

2017 ◽  
Author(s):  
Vida Ravanmehr ◽  
Minji Kim ◽  
Zhiying Wang ◽  
Olgica Milenković
Keyword(s):  
Motif Discovery ◽  
Rapid Development ◽  
Random Access ◽  
Downstream Processing ◽  
Lossy Compression ◽  
General Purpose ◽  
Supplementary Information ◽  
Peak Calling ◽  
Sequencing Technologies ◽  
Lossless And Lossy Compression

AbstractMotivationThe past decade has witnessed a rapid development of data acquisition technologies that enable integrative genomic and proteomic analysis. One such technology is chromatin immunoprecipitation sequencing (ChIP-seq), developed for analyzing interactions between proteins and DNA via next-generation sequencing technologies. As ChIP-seq experiments are inexpensive and time-efficient, massive datasets from this domain have been acquired, introducing significant storage and maintenance challenges. To address the resulting Big Data problems, we propose a state-of-the-art lossless and lossy compression framework specifically designed for ChIP-seq Wig data, termed ChIPWig. Wig is a standard file format, which in this setting contains relevant read density information crucial for visualization and downstream processing. ChIPWig may be executed in two different modes: lossless and lossy. Lossless ChIPWig compression allows for random access and fast queries in the file through careful variable-length block-wise encoding. ChIPWig also stores the summary statistics of each block needed for guided access. Lossy ChIPWig, in contrast, performs quantization of the read density values before feeding them into the lossless ChIPWig compressor. Nonuniform lossy quantization leads to further reductions in the file size, while maintaining the same accuracy of the ChIP-seq peak calling and motif discovery pipeline based on the NarrowPeaks method tailor-made for Wig files. The compressors are designed using new statistical modeling approaches coupled with delta and arithmetic encoding.ResultsWe tested the ChIPWig compressor on a number of ChIP-seq datasets generated by the ENCODE project. Lossless ChIPWig reduces the file sizes to merely 6% of the original, and offers an average 6-fold compression rate improvement compared to bigWig. The running times for compression and decompression are comparable to those of bigWig. The compression and decompression speed rates are of the order of 0.2 MB/sec using general purpose computers. ChIPWig with random access only slightly degrades the performance and running time when compared to the standard mode. In the lossy mode, the average file sizes reduce by 2-fold compared to the lossless mode. Most importantly, near-optimal nonuniform quantization with respect to mean-square distortion does not affect peak calling and motif discovery results on the data tested.Availability and ImplementationSource code and binaries freely available for download at https://github.com/vidarmehr/[email protected] informationIs available on bioRxiv.

scholarly journals New mixture models for decoy-free false discovery rate estimation in mass spectrometry proteomics

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i745-i753
Author(s):  
Yisu Peng ◽  
Shantanu Jain ◽  
Yong Fuga Li ◽  
Michal Greguš ◽  
Alexander R. Ivanov ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
False Discovery Rate ◽  
Mixture Models ◽  
Experimental Spectrum ◽  
Supplementary Information ◽  
Accurate Estimation ◽  
Component Mixture ◽  
Second Best ◽  
False Discovery ◽  
Score Distributions

Abstract Motivation Accurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, target-decoy approaches (TDAs) and decoy-free approaches (DFAs) have been widely used to estimate FDR. TDAs use a database of decoy species to faithfully model score distributions of incorrect peptide-spectrum matches (PSMs). DFAs, on the other hand, fit two-component mixture models to learn the parameters of correct and incorrect PSM score distributions. While conceptually straightforward, both approaches lead to problems in practice, particularly in experiments that push instrumentation to the limit and generate low fragmentation-efficiency and low signal-to-noise-ratio spectra. Results We introduce a new decoy-free framework for FDR estimation that generalizes present DFAs while exploiting more search data in a manner similar to TDAs. Our approach relies on multi-component mixtures, in which score distributions corresponding to the correct PSMs, best incorrect PSMs and second-best incorrect PSMs are modeled by the skew normal family. We derive EM algorithms to estimate parameters of these distributions from the scores of best and second-best PSMs associated with each experimental spectrum. We evaluate our models on multiple proteomics datasets and a HeLa cell digest case study consisting of more than a million spectra in total. We provide evidence of improved performance over existing DFAs and improved stability and speed over TDAs without any performance degradation. We propose that the new strategy has the potential to extend beyond peptide identification and reduce the need for TDA on all analytical platforms. Availabilityand implementation https://github.com/shawn-peng/FDR-estimation. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals rMETL: sensitive mobile element insertion detection with long read realignment

2019 ◽  
Vol 35 (18) ◽  
pp. 3484-3486 ◽  
Author(s):  
Tao Jiang ◽  
Bo Liu ◽  
Junyi Li ◽  
Yadong Wang
Keyword(s):  
Rapid Development ◽  
Mobile Element ◽  
Error Rates ◽  
Supplementary Information ◽  
Sequencing Error ◽  
Complex Signals ◽  
Element Insertion ◽  
Sequencing Technologies ◽  
Long Read ◽  
Mobile Element Insertion

Abstract Summary Mobile element insertion (MEI) is a major category of structure variations (SVs). The rapid development of long read sequencing technologies provides the opportunity to detect MEIs sensitively. However, the signals of MEI implied by noisy long reads are highly complex due to the repetitiveness of mobile elements as well as the high sequencing error rates. Herein, we propose the Realignment-based Mobile Element insertion detection Tool for Long read (rMETL). Benchmarking results of simulated and real datasets demonstrate that rMETL enables to handle the complex signals to discover MEIs sensitively. It is suited to produce high-quality MEI callsets in many genomics studies. Availability and implementation rMETL is available from https://github.com/hitbc/rMETL. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RECAP reveals the true statistical significance of ChIP-seq peak calls

2019 ◽  
Vol 35 (19) ◽  
pp. 3592-3598 ◽  
Author(s):  
Justin G Chitpin ◽  
Aseel Awdeh ◽  
Theodore J Perkins
Keyword(s):  
False Discovery Rate ◽  
Statistical Significance ◽  
Statistical Hypothesis ◽  
Supplementary Information ◽  
Peak Calling ◽  
Statistical Hypothesis Testing ◽  
P Values ◽  
False Discovery ◽  
Genomic Regions ◽  
And Control

Abstract Motivation Chromatin Immunopreciptation (ChIP)-seq is used extensively to identify sites of transcription factor binding or regions of epigenetic modifications to the genome. A key step in ChIP-seq analysis is peak calling, where genomic regions enriched for ChIP versus control reads are identified. Many programs have been designed to solve this task, but nearly all fall into the statistical trap of using the data twice—once to determine candidate enriched regions, and again to assess enrichment by classical statistical hypothesis testing. This double use of the data invalidates the statistical significance assigned to enriched regions, thus the true significance or reliability of peak calls remains unknown. Results Using simulated and real ChIP-seq data, we show that three well-known peak callers, MACS, SICER and diffReps, output biased P-values and false discovery rate estimates that can be many orders of magnitude too optimistic. We propose a wrapper algorithm, RECAP, that uses resampling of ChIP-seq and control data to estimate a monotone transform correcting for biases built into peak calling algorithms. When applied to null hypothesis data, where there is no enrichment between ChIP-seq and control, P-values recalibrated by RECAP are approximately uniformly distributed. On data where there is genuine enrichment, RECAP P-values give a better estimate of the true statistical significance of candidate peaks and better false discovery rate estimates, which correlate better with empirical reproducibility. RECAP is a powerful new tool for assessing the true statistical significance of ChIP-seq peak calls. Availability and implementation The RECAP software is available through www.perkinslab.ca or on github at https://github.com/theodorejperkins/RECAP. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Discovery of tandem and interspersed segmental duplications using high-throughput sequencing

2019 ◽  
Vol 35 (20) ◽  
pp. 3923-3930 ◽  
Author(s):  
Arda Soylev ◽  
Thong Minh Le ◽  
Hajar Amini ◽  
Can Alkan ◽  
Fereydoun Hormozdiari
Keyword(s):  
False Discovery Rate ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Real Data ◽  
Read Depth ◽  
Supplementary Information ◽  
Segmental Duplications ◽  
Structural Variations ◽  
Multiple Sequence ◽  
False Discovery

Abstract Motivation Several algorithms have been developed that use high-throughput sequencing technology to characterize structural variations (SVs). Most of the existing approaches focus on detecting relatively simple types of SVs such as insertions, deletions and short inversions. In fact, complex SVs are of crucial importance and several have been associated with genomic disorders. To better understand the contribution of complex SVs to human disease, we need new algorithms to accurately discover and genotype such variants. Additionally, due to similar sequencing signatures, inverted duplications or gene conversion events that include inverted segmental duplications are often characterized as simple inversions, likewise, duplications and gene conversions in direct orientation may be called as simple deletions. Therefore, there is still a need for accurate algorithms to fully characterize complex SVs and thus improve calling accuracy of more simple variants. Results We developed novel algorithms to accurately characterize tandem, direct and inverted interspersed segmental duplications using short read whole genome sequencing datasets. We integrated these methods to our TARDIS tool, which is now capable of detecting various types of SVs using multiple sequence signatures such as read pair, read depth and split read. We evaluated the prediction performance of our algorithms through several experiments using both simulated and real datasets. In the simulation experiments, using a 30× coverage TARDIS achieved 96% sensitivity with only 4% false discovery rate. For experiments that involve real data, we used two haploid genomes (CHM1 and CHM13) and one human genome (NA12878) from the Illumina Platinum Genomes set. Comparison of our results with orthogonal PacBio call sets from the same genomes revealed higher accuracy for TARDIS than state-of-the-art methods. Furthermore, we showed a surprisingly low false discovery rate of our approach for discovery of tandem, direct and inverted interspersed segmental duplications prediction on CHM1 (<5% for the top 50 predictions). Availability and implementation TARDIS source code is available at https://github.com/BilkentCompGen/tardis, and a corresponding Docker image is available at https://hub.docker.com/r/alkanlab/tardis/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Interpretation of ‘Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes

2018 ◽  
Author(s):  
Qike Li ◽  
Samir Rachid Zaim ◽  
Dillon Aberasturi ◽  
Joanne Berghout ◽  
Haiquan Li ◽  
...  
Keyword(s):  
False Discovery Rate ◽  
Rna Sequencing ◽  
Mixture Model ◽  
Rate Control ◽  
Empirical Bayes ◽  
Single Subject ◽  
Local False Discovery Rate ◽  
Simulation Studies ◽  
Extensive Simulation ◽  
False Discovery

AbstractCalculating Differentially Expressed Genes (DEGs) from RNA-sequencing requires replicates to estimate gene-wise variability, infeasible in clinics. By imposing restrictive transcriptome-wide assumptions limiting inferential opportunities of conventional methods (edgeR, NOISeq-sim, DESeq, DEGseq), comparing two conditions without replicates (TCWR) has been proposed, but not evaluated. Under TCWR conditions (e.g., unaffected tissue vs. tumor), differences of transformed expression of the proposed individualized DEG (iDEG) method follow a distribution calculated across a local partition of related transcripts at baseline expression; thereafter the probability of each DEG is estimated by empirical Bayes with local false discovery rate control using a two-group mixture model. In extensive simulation studies of TCWR methods, iDEG and NOISeq are more accurate at 5%<DEGs<20% (precision>90%, recall>75%, false_positive_rate<1%) and 30%<DEGs<40% (precision=recall∼90%), respectively.The proposed iDEG method borrows localized distribution information from the same individual, a strategy that improves accuracy to compare transcriptomes in absence of replicates at low DEGs conditions. http://www.lussiergroup.org/publications/iDEG

scholarly journals LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control

2021 ◽  
Author(s):  
Yingtian Hu ◽  
Glen Satten ◽  
Yijuan Hu
Keyword(s):  
Logistic Regression ◽  
False Discovery Rate ◽  
Compositional Analysis ◽  
R Package ◽  
Small Sample ◽  
Metagenomic Sequencing ◽  
False Discovery ◽  
Small Sample Sizes ◽  
Low Sensitivity ◽  
Microbiome Data

Abstract Motivation: Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant", while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods of compositional analysis such as ANCOM or ANCOM-BC use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren, Willis and Callahan [1] have recently proposed a model for how these biases affect relative abundance data. Methods: Motivated by [1], we show that the (log) odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose LOCOM, a robust logistic regression approach to compositional analysis, that does not require pseudocounts. We use a Firth bias-corrected estimating function to account for sparse data. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for continuous and/or discrete confounding covariates is supported. Results: Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, ANCOM often had inflated FDR; ANCOM-BC largely controlled FDR but still had modest inflation occasionally; ALDEx2 generally had low sensitivity. LOCOM and ANCOM were robust to experimental biases in every situation, while ANCOM-BC and ALDEx2 had elevated FDR when biases at causal and non-causal taxa were differentially distributed. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Availability and implementation: Our R package LOCOM is available on GitHub at https://github.com/yijuanhu/LOCOM in formats appropriate for Macintosh or Windows.

scholarly journals LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control

2021 ◽  
Author(s):  
Yingtian Hu ◽  
Glen A. Satten ◽  
Yi-Juan Hu
Keyword(s):  
Logistic Regression ◽  
False Discovery Rate ◽  
Compositional Analysis ◽  
R Package ◽  
Small Sample ◽  
Metagenomic Sequencing ◽  
False Discovery ◽  
Small Sample Sizes ◽  
Low Sensitivity ◽  
Microbiome Data

AbstractMotivationCompositional analysis is based on the premise that a relatively small proportion of taxa are “differentially abundant”, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods of compositional analysis such as ANCOM or ANCOM-BC use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren, Willis and Callahan [1] have recently proposed a model for how these biases affect relative abundance data.MethodsMotivated by [1], we show that the (log) odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose LOCOM, a robust logistic regression approach to compositional analysis, that does not require pseudocounts. We use a Firth bias-corrected estimating function to account for sparse data. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for continuous and/or discrete confounding covariates is supported.ResultsOur simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, ANCOM often had inflated FDR; ANCOM-BC largely controlled FDR but still had modest inflation occasionally; ALDEx2 generally had low sensitivity. LOCOM and ANCOM were robust to experimental biases in every situation, while ANCOM-BC and ALDEx2 had elevated FDR when biases at causal and non-causal taxa were differentially distributed. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies.Availability and implementationOur R package LOCOM is available on GitHub at https://github.com/yijuanhu/LOCOM in formats appropriate for Macintosh or Windows.

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