Biology subjects

Appuswamy, R.

Publications and source records attributed to Appuswamy, R..

5 recordsLinked to original sources

bioRxiv2025

Accurate Variant Classification in Tumour-Only Genomic Data Using Interpretable Tabular Models

Recent work has shown that machine learning can provide a reliable tool to classify somatic and rare germline variants in cancer studies where matched-normal samples are not available. Here, we present a workflow that combines an opensource pipeline with three machine-learning models, XGBoost, LightGBM, and TabNet, trained on eight types of features. Our approach substantially enhances the accuracy across all tested models providing accurate results irrespective of sample ancestry and tumour type. We build a parsimonious model and demonstrate that training on low-coverage data retains high accuracy when applied to high-coverage data and vice versa. In contrast to previous findings, our results indicate that XGBoost slightly outperforms LightGBM, achieving high classification accuracy even in the absence of copy-number information and allowing for the ancestry-unbiased calculation of the tumour mutational burden for different types of cancer.

bioinformatics↗

bioRxiv2023

Scaling Logical Density of DNA storage with Enzymatically-Ligated Composite Motifs

DNA is a promising candidate for long-term data storage due to its high density and endurance. The key challenge in DNA storage today is the cost of synthesis. In this work, we propose composite motifs, a frame-work that uses a mixture of prefabricated motifs as building blocks to reduce synthesis cost by scaling logical density. To write data, we introduce Bridge Oligonucleotide Assembly, an enzymatic ligation technique for synthesizing oligos based on composite motifs. To sequence data, we introduce Direct Oligonucleotide Sequencing, a nanopore-based technique to sequence oligos without assembly and amplification. To decode data, we introduce Motif-Search, a novel consensus caller that provides accurate reconstruction despite synthesis and sequencing errors. Using the proposed methods, we present an end-to-end experiment where we store the text "HelloWorld" at a logical density of 84 bits/cycle (14-42x improvement over state-of-the-art.)

synthetic biology↗

bioRxiv2022

OligoArchive-DSM: Columnar Design for Error-Tolerant Database Archival using Synthetic DNA

The surge in demand for cost-effective, durable long-term archival media, coupled with density limitations of contemporary magnetic media, has resulted in synthetic DNA emerging as a promising new alternative. Today, the limiting factor for DNA-based data archival is the cost of writing (synthesis) and reading (sequencing) DNA. Newer techniques that reduce the cost often do so at the expense of reliability, as they introduce complex, technology-specific error patterns. In order to deal with such errors, it is important to design efficient pipelines that can carefully use redundancy to mask errors without amplifying overall cost. In this paper, we present OligoArchive-DSM (OA-DSM), an end-to-end DNA archival pipeline that can provide error-tolerant data storage at low read/write costs. Central to OA-DSM is a database-inspired columnar encoding technique that makes it possible to improve efficiency by enabling integrated decoding and consensus calling during data restoration.

bioengineering↗

bioRxiv2020

Accel-Align: A Fast Sequence Mapper and Aligner based on the Seed-Embed-Extend Method

BackgroundImprovements in sequencing technology continue to drive sequencing cost towards $100 per genome. However, mapping sequenced data to a reference genome remains a computationally-intensive task due to the dependence on edit distance for dealing with indels and mismatches introduced by sequencing. All modern aligners use seed-filter-extend (SFE) methodology and rely on filtration heuristics to reduce the overhead of edit distance computation. However, filtering has inherent performance-accuracy trade-offs that limits its effectiveness. ResultsMotivated by algorithmic advances in randomized low-distortion embedding, we introduce seed- embed-extend (SEE), a new methodology for developing sequence mappers and aligners. While SFE focuses on eliminating sub-optimal candidates, SEE focuses instead on identifying optimal candidates. To do so, SEE transforms the read and reference strings from edit distance regime to the Hamming regime by embedding them using a randomized algorithm, and uses Hamming distance over the embedded set to identify optimal candidates. To show that SEE performs well in practice, we present Accel-Align, an SEE-based short-read sequence mapper and aligner that is 3-12x faster than state-of-the-art aligners on commodity CPUs, without any special-purpose hardware, while providing comparable accuracy. ConclusionsAs sequencing technologies continue to increase read length while improving throughput and accuracy, we believe that randomized embeddings open up new avenues for optimization that cannot be achieved by using edit distance. Thus, the techniques presented in this paper have a much broader scope as they can be used for other applications like graph alignment, multiple sequence alignment, and sequence assembly. Availabilityhttps://github.com/raja-appuswamy/accel-align-release

bioinformatics↗

bioRxiv2018

Sequence Alignment Through the Looking Glass

Rapid advances in sequencing technologies are producing genomic data on an unprecedented scale. The first, and often one of the most time consuming, step of genomic data analysis is sequence alignment, where sequenced reads must be aligned to a reference genome. Several years of research on alignment algorithms has led to the development of several state-of-the-art sequence aligners that can map tens of thousands of reads per second.\n\nIn this work, we answer the question \"How do sequence aligners utilize modern processors?\" We examine four state-of-the-art aligners running on an Intel processor and identify that all aligners leave the processor substantially underutilized. We perform an in-depth microarchitectural analysis to explore the interaction between aligner software and processor hardware. We identify bottlenecks that lead to processor underutilization and discuss the implications of our analysis on next-generation sequence aligner design.

bioinformatics↗