Biology subjects

Niranjan Nagarajan

Publications and source records attributed to Niranjan Nagarajan.

6 recordsLinked to original sources

bioRxiv2016

Fast and accurate de novo genome assembly from long uncorrected reads

The assembly of long reads from Pacific Biosciences and Oxford Nanopore Technologies typically requires resource intensive error correction and consensus generation steps to obtain high quality assemblies. We show that the error correction step can be omitted and high quality consensus sequences can be generated efficiently with a SIMD accelerated, partial order alignment based stand-alone consensus module called Racon. Based on tests with PacBio and Oxford Nanopore datasets we show that Racon coupled with Miniasm enables consensus genomes with similar or better quality than state-of-the-art methods while being an order of magnitude faster.\n\nRacon is available open source under the MIT license at https://github.com/isovic/racon.git.

Bioinformatics↗

bioRxiv2016

INC-Seq: Accurate single molecule reads using nanopore sequencing

Nanopore sequencing provides a rapid, cheap and portable real-time sequencing platform with the potential to revolutionize genomics. Several applications, including RNA-seq, haplotype sequencing and 16S sequencing, are however limited by its relatively high single read error rate (>10%). We present INC-Seq (Intramolecular-ligated Nanopore Consensus Sequencing) as a strategy for obtaining long and accurate nanopore reads starting with low input DNA. Applying INC-Seq for 16S rRNA based bacterial profiling generated full-length amplicon sequences with median accuracy >97%. INC-Seq reads enable accurate species-level classification, identification of species at 0.1% abundance and robust quantification of relative abundances, providing a cheap and effective approach for pathogen detection and microbiome profiling on the MinION system.

Genomics↗

bioRxiv2015

Fast and sensitive mapping of error-prone nanopore sequencing reads with GraphMap

Exploiting the power of nanopore sequencing requires the development of new bioinformatics approaches to deal with its specific error characteristics. We present the first nanopore read mapper (GraphMap) that uses a read-funneling paradigm to robustly handle variable error rates and fast graph traversal to align long reads with speed and very high precision (>95%). Evaluation on MinION sequencing datasets against short and long-read mappers indicates that GraphMap increases mapping sensitivity by at least 15-80%. GraphMap alignments are the first to demonstrate consensus calling with <1 error in 100,000 bases, variant calling on the human genome with 76% improvement in sensitivity over the next best mapper (BWA-MEM), precise detection of structural variants from 100bp to 4kbp in length and species and strain-specific identification of pathogens using MinION reads. GraphMap is available open source under the MIT license at https://github.com/isovic/graphmap.

Genomics↗

bioRxiv2015

OPERA-LG: Efficient and exact scaffolding of large, repeat-rich eukaryotic genomes with performance guarantees

The assembly of large, repeat-rich eukaryotic genomes continues to represent a significant challenge in genomics. While long-read technologies have made the high-quality assembly of small, microbial genomes increasingly feasible, data generation can be prohibitively expensive for larger genomes. Advances in assembly algorithms are thus essential to exploit the characteristics of short and long-read sequencing technologies to consistently and reliably provide high-quality assemblies in a cost-efficient manner. OPERA-LG is a scalable, exact algorithm for the scaffold assembly of large, repeat-rich genomes, with consistent improvement over state-of-the-art programs for scaffold correctness and contiguity. It provides a rigorous framework for scaffolding of repetitive sequences and a systematic approach for combining data from different second-generation (Illumina, Ion Torrent) and third-generation (PacBio, ONT) sequencing technologies. OPERA-LG efficiently scaffolds large genomes with provable scaffold properties, providing an avenue for systematic augmentation and improvement of 1000s of existing draft eukaryotic genome assemblies.

Bioinformatics↗

bioRxiv2015

Index-based map-to-sequence alignment in large eukaryotic genomes

Resolution of complex repeat structures and rearrangements in the assembly and analysis of large eukaryotic genomes is often aided by a combination of high-throughput sequencing and mapping technologies (e.g. optical restriction mapping). In particular, mapping technologies can generate sparse maps of large DNA fragments (150 kbp-2 Mbp) and thus provide a unique source of information for disambiguating complex rearrangements in cancer genomes. Despite their utility, combining high-throughput sequencing and mapping technologies has been challenging due to the lack of efficient and freely available software for robustly aligning maps to sequences. Here we introduce two new map-to-sequence alignment algorithms that efficiently and accurately align high-throughput mapping datasets to large, eukaryotic genomes while accounting for high error rates. In order to do so, these methods (OPTIMA for glocal and OPTIMA-Overlap for overlap alignment) exploit the ability to create efficient data structures that index continuous-valued mapping data while accounting for errors. We also introduce an approach for evaluating the significance of alignments that avoids expensive permutation-based tests while being agnostic to technology-dependent error rates. Our benchmarking results suggest that OPTIMA and OPTIMA-Overlap outperform state-of-the-art approaches in sensitivity (1.6-2x improvement) while simultaneously being more efficient (170-200%) and precise in their alignments (99% precision). These advantages are independent of the quality of the data, suggesting that our indexing approach and statistical evaluation are robust and provide improved sensitivity while guaranteeing high precision.

Bioinformatics↗

bioRxiv2014

The importance of study design for detecting differentially abundant features in high-throughput experiments

The use of high-throughput experiments, such as RNA-seq, to simultaneously identify differentially abundant entities across conditions has become widespread, but the systematic planning of such studies is currently hampered by the lack of general-purpose tools to do so. Here we demonstrate that there is substantial variability in performance across statistical tests, normalization techniques and study conditions, potentially leading to significant wastage of resources and/or missing information in the absence of careful study design. We present a broadly applicable experimental design tool called EDDA, and the first for single-cell RNA-seq, Nanostring and Metagenomic studies, that can be used to i) rationally choose from a panel of statistical tests, ii) measure expected performance for a study and iii) plan experiments to minimize mis-utilization of valuable resources. Using case studies from recent single-cell RNA-seq, Nanostring and Metagenomics studies, we highlight its general utility and, in particular, show a) the ability to correctly model single-cell RNA-seq data and do comparisons with 1/5th the amount of sequencing currently used and b) that the selection of suitable statistical tests strongly impacts the ability to detect biomarkers in Metagenomic studies. Furthermore, we demonstrate that a novel mode-based normalization employed in EDDA uniformly improves in robustness over existing approaches (10-20%) and increases precision to detect differential abundance by up to 140%.

Bioinformatics↗