Biology subjects

Jain, C.

Publications and source records attributed to Jain, C..

34 records · Page 2Linked to original sources

bioRxiv2021

LBH is a cancer stem cell- and metastasis-driving oncogene essential for WNT stem cell function in breast cancer

Cancer stem cells (CSCs) initiate tumors, resist treatment, and seed lethal metastases; yet CSC-specific treatments are lacking. Aggressive, treatment-resistant triple-negative breast cancers (TNBC) exhibit WNT pathway activation and are CSC enriched. Here, we show that Limb-Bud- and-Heart (LBH), a WNT/{beta}-catenin target required for normal mammary stem cell self-renewal, marks poor prognosis, stem-like TNBC, and is a key controller of breast cancer stemness. LBH is specifically expressed in tumor-initiating CD44+CD24-/low breast CSCs. LBH overexpression confers stem-like, metastatic traits on both TNBC and luminal origin, non-TNBC breast cancer cells by activating stem cell transcriptional programs. Importantly, silencing LBH potently suppresses tumor initiation and metastasis in vivo, and sensitizes TNBC cells to chemotherapy. LBH knockout in the MMTV-Wnt1 breast cancer mouse model, furthermore, revealed LBH is required for WNT-driven breast CSC expansion. Our findings identify LBH as an essential CSC driver downstream of WNT, and a new molecular target for anti-cancer stem cell therapy.

cancer biology↗

bioRxiv2020

A long read mapping method for highly repetitive reference sequences

About 5-10% of the human genome remains inaccessible for functional analysis due to the presence of repetitive sequences such as segmental duplications and tandem repeat arrays. To enable high-quality resequencing of personal genomes, it is crucial to support end-to-end genome variant discovery using repeat-aware read mapping methods. In this study, we highlight the fact that existing long read mappers often yield incorrect alignments and variant calls within long, near-identical repeats, as they remain vulnerable to allelic bias. In the presence of a non-reference allele within a repeat, a read sampled from that region could be mapped to an incorrect repeat copy because the standard pairwise sequence alignment scoring system penalizes true variants. To address the above problem, we propose a novel, long read mapping method that addresses allelic bias by making use of minimal confidently alignable substrings (MCASs). MCASs are formulated as minimal length substrings of a read that have unique alignments to a reference locus with sufficient mapping confidence (i.e., a mapping quality score above a user-specified threshold). This approach treats each read mapping as a collection of confident sub-alignments, which is more tolerant of structural variation and more sensitive to paralog-specific variants (PSVs) within repeats. We mathematically define MCASs and discuss an exact algorithm as well as a practical heuristic to compute them. The proposed method, referred to as Winnowmap2, is evaluated using simulated as well as real long read benchmarks using the recently completed gapless assemblies of human chromosomes X and 8 as a reference. We show that Winnowmap2 successfully addresses the issue of allelic bias, enabling more accurate downstream variant calls in repetitive sequences. As an example, using simulated PacBio HiFi reads and structural variants in chromosome 8, Winnowmap2 alignments achieved the lowest false-negative and false-positive rates (1.89%, 1.89%) for calling structural variants within near-identical repeats compared to minimap2 (39.62%, 5.88%) and NGMLR (56.60%, 36.11%) respectively. Winnowmap2 code is accessible at https://github.com/marbl/Winnowmap

bioinformatics↗

bioRxiv2020

The structure, function, and evolution of a complete human chromosome 8

The complete assembly of each human chromosome is essential for understanding human biology and evolution. Using complementary long-read sequencing technologies, we complete the first linear assembly of a human autosome, chromosome 8. Our assembly resolves the sequence of five previously long-standing gaps, including a 2.08 Mbp centromeric -satellite array, a 644 kbp defensin copy number polymorphism important for disease risk, and an 863 kbp variable number tandem repeat at chromosome 8q21.2 that can function as a neocentromere. We show that the centromeric -satellite array is generally methylated except for a 73 kbp hypomethylated region of diverse higher-order -satellite enriched with CENP-A nucleosomes, consistent with the location of the kinetochore. Using a dual long-read sequencing approach, we complete the assembly of the orthologous chromosome 8 centromeric regions in chimpanzee, orangutan, and macaque for the first time to reconstruct its evolutionary history. Comparative and phylogenetic analyses show that the higher-order -satellite structure evolved specifically in the great ape ancestor, and the centromeric region evolved with a layered symmetry, with more ancient higher-order repeats located at the periphery adjacent to monomeric -satellites. We estimate that the mutation rate of centromeric satellite DNA is accelerated at least 2.2-fold, and this acceleration extends beyond the higher-order -satellite into the flanking sequence.

genomics↗

bioRxiv2020

Implementation of PAR-CLIP to characterize RNA-protein interactions in prokaryotes at nucleotide resolution

The identification of RNAs that are recognized by RNA-binding proteins (RNA-BPs) using techniques such as "Crosslinking and Immunoprecipitation" (CLIP) has revolutionized the genome-wide discovery of RNA-BP RNA targets. Among the different versions of CLIP that have been developed, the use of photoactivable nucleoside analogs incorporated into cellular RNA has resulted in high efficiency photoactivable ribonucleoside-enhanced CLIP (PAR-CLIP). Nonetheless, PAR-CLIP has not yet been applied in prokaryotes. To determine if PAR-CLIP can be used in prokaryotes, we determined suitable conditions for the incorporation of 4-thiouridine (4SU), a photoactivable nucleoside, into E. coli RNA, and for the isolation of crosslinked RNA. Applying this technique to Hfq, a well-characterized regulator of small RNA (sRNA)-messenger RNA (mRNA) interactions, we showed that PAR-CLIP identified most of the known sRNA targets of Hfq, as well as functionally relevant sites of Hfq-mRNA interactions at nucleotide resolution. Based on our findings, PAR-CLIP represents an improved method to identify both the RNAs and the specific regulatory sites that are recognized by RNA-BPs in prokaryotes.

microbiology↗

bioRxiv2020

Dual-level autoregulation of the E. coli DeaD RNA helicase via mRNA stability and Rho-dependent transcription termination

DEAD-box proteins (DBPs) are RNA remodeling factors associated with RNA helicase activity that are found in nearly all organisms. Despite extensive studies on the mechanisms used by DBPs to regulate RNA function, very little is known about how DBPs themselves are regulated. In this work, we have analyzed the expression and regulation of DeaD/CsdA, the largest of the DBPs in Escherichia coli (E. coli). We show that deaD transcription initiates 838 nts upstream of the start of the coding region. We have also found that DeaD is autoregulated through a negative feedback mechanism that operates both at the level of mRNA stability and Rho-dependent transcription termination, and this regulation is dependent upon the 5 untranslated region (5 UTR). These findings suggest that DeaD might be regulating the conformation of its own mRNA through its RNA helicase activity to facilitate ribonuclease and Rho access to its 5UTR.

microbiology↗

bioRxiv2020

Weighted minimizer sampling improves long read mapping

MotivationIn this era of exponential data growth, minimizer sampling has become a standard algorithmic technique for rapid genome sequence comparison. This technique yields a sub-linear representation of sequences, enabling their comparison in reduced space and time. A key property of the minimizer technique is that if two sequences share a substring of a specified length, then they can be guaranteed to have a matching minimizer. However, because the k-mer distribution in eukaryotic genomes is highly uneven, minimizer-based tools (e.g., Minimap2, Mashmap) opt to discard the most frequently occurring minimizers from the genome in order to avoid excessive false positives. By doing so, the underlying guarantee is lost and accuracy is reduced in repetitive genomic regions. ResultsWe introduce a novel weighted-minimizer sampling algorithm. A unique feature of the proposed algorithm is that it performs minimizer sampling while taking into account a weight for each k-mer; i.e, the higher the weight of a k-mer, the more likely it is to be selected. By down-weighting frequently occurring k-mers, we are able to meet both objectives: (i) avoid excessive false-positive matches, and (ii) maintain the minimizer match guarantee. We tested our algorithm, Winnowmap, using both simulated and real long-read data and compared it to a state-of-the-art long read mapper, Minimap2. Our results demonstrate a reduction in the mapping error-rate from 0.14% to 0.06% in the recently finished human X chromosome (154.3 Mbp), and from 3.6% to 0% within the highly repetitive X centromere (3.1 Mbp). Winnowmap improves mapping accuracy within repeats and achieves these results with sparser sampling, leading to better index compression and competitive runtimes. Contactadam.phillippy@nih.gov AvailabilityWinnowmap is built on top of the Minimap2 codebase (Li, 2018) and is available at https://github.com/marbl/winnowmap.

bioinformatics↗

bioRxiv2019

Development of PAR-CLIP to analyze RNA-protein interactions in prokaryotes

The ability to identify RNAs that are recognized by RNA-binding proteins (RNA-BPs) using techniques such as "Crosslinking and Immunoprecipitation" (CLIP) has revolutionized the genome-wide discovery of RNA targets. Among the different versions of CLIP developed, the incorporation of photoactivable nucleoside analogs into cellular RNA has proven to be especially valuable, allowing for high efficiency photoactivable ribonucleoside-enhanced CLIP (PAR-CLIP). Although PAR-CLIP has become an established technique for use in eukaryotes, it has not yet been applied in prokaryotes. To determine if PAR-CLIP can be used in prokaryotes, we first investigated whether 4-thiouridine (4SU), a photoactivable nucleoside, can be incorporated into E. coli RNA. After determining 4SU incorporation into RNA, we developed suitable conditions for crosslinking of proteins in E. coli cells and for the isolation of crosslinked RNA. Applying this technique to Hfq, a well-characterized regulator of small RNA (sRNA) - messenger RNA (mRNA) interactions, we showed that PAR-CLIP identified most of the known sRNA targets of Hfq. Based on our results, PAR-CLIP represents an improved method to identify the RNAs recognized by RNA-BPs in prokaryotes.

microbiology↗

bioRxiv2019

Validating paired-end read alignments in sequence graphs

Graph based non-linear reference structures such as variation graphs and colored de Bruijn graphs enable incorporation of full genomic diversity within a population. However, transitioning from a simple string-based reference to graphs requires addressing many computational challenges, one of which concerns accurately mapping sequencing read sets to graphs. Paired-end Illumina sequencing is a commonly used sequencing platform in genomics, where the paired-end distance constraints allow disambiguation of repeats. Many recent works have explored provably good index-based and alignment-based strategies for mapping individual reads to graphs. However, validating distance constraints efficiently over graphs is not trivial, and existing sequence to graph mappers rely on heuristics. We introduce a mathematical formulation of the problem, and provide a new algorithm to solve it exactly. We take advantage of the high sparsity of reference graphs, and use sparse matrix-matrix multiplications (SpGEMM) to build an index which can be queried efficiently by a mapping algorithm for validating the distance constraints. Effectiveness of the algorithm is demonstrated using real reference graphs, including a human MHC variation graph, and a pan-genome de-Bruijn graph built using genomes of 20 B. anthracis strains. While the one-time indexing time can vary from a few minutes to a few hours using our algorithm, answering a million distance queries takes less than a second.\n\n2012 ACM Subject ClassificationMathematics of computing [->] Paths and connectivity problems; Applied computing [->] Computational genomics

bioinformatics↗

bioRxiv2019

Accelerating Sequence Alignment to Graphs

Aligning DNA sequences to an annotated reference is a key step for genotyping in biology. Recent scientific studies have demonstrated improved inference by aligning reads to a variation graph, i.e., a reference sequence augmented with known genetic variations. Given a variation graph in the form of a directed acyclic string graph, the sequence to graph alignment problem seeks to find the best matching path in the graph for an input query sequence. Solving this problem exactly using a sequential dynamic programming algorithm takes quadratic time in terms of the graph size and query length, making it difficult to scale to high throughput DNA sequencing data. In this work, we propose the first parallel algorithm for computing sequence to graph alignments that leverages multiple cores and single-instruction multiple-data (SIMD) operations. We take advantage of the available inter-task parallelism, and provide a novel blocked approach to compute the score matrix while ensuring high memory locality. Using a 48-core Intel Xeon Skylake processor, the proposed algorithm achieves peak performance of 317 billion cell updates per second (GCUPS), and demonstrates near linear weak and strong scaling on up to 48 cores. It delivers significant performance gains compared to existing algorithms, and results in run-time reduction from multiple days to three hours for the problem of optimally aligning high coverage long (PacBio/ONT) or short (Illumina) DNA reads to an MHC human variation graph containing 10 million vertices.\n\nAvailabilityThe implementation of our algorithm is available at https://github.com/ParBLiSS/PaSGAL. Data sets used for evaluation are accessible using https://alurulab.cc.gatech.edu/PaSGAL.

bioinformatics↗

bioRxiv2019

On the Complexity of Sequence to Graph Alignment

Availability of extensive genetics data across multiple individuals and populations is driving the growing importance of graph based reference representations. Aligning sequences to graphs is a fundamental operation on several types of sequence graphs (variation graphs, assembly graphs, pan-genomes, etc.) and their biological applications. Though research on sequence to graph alignments is nascent, it can draw from related work on pattern matching in hypertext. In this paper, we study sequence to graph alignment problems under Hamming and edit distance models, and linear and affine gap penalty functions, for multiple variants of the problem that allow changes in query alone, graph alone, or in both. We prove that when changes are permitted in graphs either standalone or in conjunction with changes in the query, the sequence to graph alignment problem is [Formula]-complete under both Hamming and edit distance models for alphabets of size [≥] 2. For the case where only changes to the sequence are permitted, we present an O(|V| + m|E|) time algorithm, where m denotes the query size, and V and E denote the vertex and edge sets of the graph, respectively. Our result is generalizable to both linear and affine gap penalty functions, and improves upon the run-time complexity of existing algorithms.

bioinformatics↗

bioRxiv2019

A comprehensive evaluation of long read error correction methods

BackgroundThird-generation single molecule sequencing technologies can sequence long reads, which is advancing the frontiers of genomics research. However, their high error rates prohibit accurate and efficient downstream analysis. This difficulty has motivated the development of many long read error correction tools, which tackle this problem through sampling redundancy and/or leveraging accurate short reads of the same biological samples. Existing studies to asses these tools use simulated data sets, and are not sufficiently comprehensive in the range of software covered or diversity of evaluation measures used.\n\nResultsIn this paper, we present a categorization and review of long read error correction methods, and provide a comprehensive evaluation of the corresponding long read error correction tools. Leveraging recent real sequencing data, we establish benchmark data sets and set up evaluation criteria for a comparative assessment which includes quality of error correction as well as run-time and memory usage. We study how trimming and long read sequencing depth affect error correction in terms of length distribution and genome coverage post-correction, and the impact of error correction performance on an important application of long reads, genome assembly. We provide guidelines for practitioners for choosing among the available error correction tools and identify directions for future research.\n\nConclusionsDespite the high error rate of long reads, the state-of-the-art correction tools can achieve high correction quality. When short reads are available, the best hybrid methods outperform non-hybrid methods in terms of correction quality and computing resource usage. When choosing tools for use, practitioners are suggested to be careful with a few correction tools that discard reads, and check the effect of error correction tools on downstream analysis. Our evaluation code is available as open-source at https://github.com/haowenz/LRECE.

bioinformatics↗

bioRxiv2018

MetaMaps - Strain-level metagenomic assignment and compositional estimation for long reads

Metagenomic sequence classification should be fast, accurate and information-rich. Emerging long-read sequencing technologies promise to improve the balance between these factors but most existing methods were designed for short reads. MetaMaps is a new method, specifically developed for long reads, that combines the accuracy of slower alignment-based methods with the scalability of faster k-mer-based methods. Using an approximate mapping algorithm, it is capable of mapping a long-read metagenome to a comprehensive RefSeq database with >12,000 genomes in <30 GB or RAM on a laptop computer. Integrating these mappings with a probabilistic scoring scheme and EM-based estimation of sample composition, MetaMaps achieves >95% accuracy for species-level read assignment and r2 > 0.98 for the estimation of sample composition on both simulated and real data. Uniquely, MetaMaps outputs mapping locations and qualities for all classified reads, enabling functional studies (e.g. gene presence/absence) and the detection of novel species not present in the current database.\n\nAvailability and ImplementationMetaMaps is implemented in C++/Perl and freely available from https://github.com/DiltheyLab/MetaMaps (GPL v3).

bioinformatics↗

bioRxiv2018

A Fast Adaptive Algorithm for Computing Whole-Genome Homology Maps

MotivationWhole-genome alignment is an important problem in genomics for comparing different species, mapping draft assemblies to reference genomes, and identifying repeats. However, for large plant and animal genomes, this task remains compute and memory intensive.\n\nResultsWe introduce an approximate algorithm for computing local alignment boundaries between long DNA sequences. Given a minimum alignment length and an identity threshold, our algorithm computes the desired alignment boundaries and identity estimates using kmer-based statistics, and maintains sufficient probabilistic guarantees on the output sensitivity. Further, to prioritize higher scoring alignment intervals, we develop a plane-sweep based filtering technique which is theoretically optimal and practically efficient. Implementation of these ideas resulted in a fast and accurate assembly-to-genome and genome-to-genome mapper. As a result, we were able to map an error-corrected whole-genome NA12878 human assembly to the hg38 human reference genome in about one minute total execution time and < 4 GB memory using 8 CPU threads, achieving more than an order of magnitude improvement in both runtime and memory over competing methods. Recall accuracy of computed alignment boundaries was consistently found to be > 97% on multiple datasets. Finally, we performed a sensitive self-alignment of the human genome to compute all duplications of length [≥] 1 Kbp and [≥] 90% identity. The reported output achieves good recall and covers 5% more bases than the current UCSC genome browsers segmental duplication annotation.\n\nAvailabilityhttps://github.com/marbl/MashMap\n\nContactadam.phillippy@nih.gov, aluru@cc.gatech.edu

bioinformatics↗

bioRxiv2017

High-throughput ANI Analysis of 90K Prokaryotic Genomes Reveals Clear Species Boundaries

A fundamental question in microbiology is whether there is a continuum of genetic diversity among genomes or clear species boundaries prevail instead. Answering this question requires robust measurement of whole-genome relatedness among thousands of genomes and from diverge phylogenetic lineages. Whole-genome similarity metrics such as Average Nucleotide Identity (ANI) can provide the resolution needed for this task, overcoming several limitations of traditional techniques used for the same purposes. Although the number of genomes currently available may be adequate, the associated bioinformatics tools for analysis are lagging behind these developments and cannot scale to large datasets. Here, we present a new method, FastANI, to compute ANI using alignment-free approximate sequence mapping. Our analyses demonstrate that FastANI produces an accurate ANI estimate and is up to three orders of magnitude faster when compared to an alignment (e.g., BLAST)-based approach. We leverage FastANI to compute pairwise ANI values among all prokaryotic genomes available in the NCBI database. Our results reveal a clear genetic discontinuity among the database genomes, with 99.8% of the total 8 billion genome pairs analyzed showing either >95% intra-species ANI or <83% inter-species ANI values. We further show that this discontinuity is recovered with or without the most frequently represented species in the database and is robust to historic additions in the public genome databases. Therefore, 95% ANI represents an accurate threshold for demarcating almost all currently named prokaryotic species, and wide species boundaries may exist for prokaryotes.

bioinformatics↗

bioRxiv2017

A fast approximate algorithm for mapping long reads to large reference databases

Emerging single-molecule sequencing technologies from Pacific Biosciences and Oxford Nanopore have revived interest in long read mapping algorithms. Alignment-based seed-and-extend methods demonstrate good accuracy, but face limited scalability, while faster alignment-free methods typically trade decreased precision for efficiency. In this paper, we combine a fast approximate read mapping algorithm based on minimizers with a novel MinHash identity estimation technique to achieve both scalability and precision. In contrast to prior methods, we develop a mathematical framework that defines the types of mapping targets we uncover, establish probabilistic estimates of p-value and sensitivity, and demonstrate tolerance for alignment error rates up to 20%. With this framework, our algorithm automatically adapts to different minimum length and identity requirements and provides both positional and identity estimates for each mapping reported. For mapping human PacBio reads to the hg38 reference, our method is 290x faster than BWA-MEM with a lower memory footprint and recall rate of 96%. We further demonstrate the scalability of our method by mapping noisy PacBio reads (each [≥] 5 kbp in length) to the complete NCBI RefSeq database containing 838 Gbp of sequence and > 60, 000 genomes.

bioinformatics↗

bioRxiv2017

Critical Assessment of Metagenome Interpretation - a benchmark of computational metagenomics software

In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.

bioinformatics↗