Biology subjects

Almodaresi, F.

Publications and source records attributed to Almodaresi, F..

7 recordsLinked to original sources

bioRxiv2025

React-to-Me: A Conversational Interface for Interactive Exploration of the Reactome Pathway Knowledgebase.

The Reactome Pathway Knowledgebase (www.reactome.org) provides expert-curated information on human biological pathways, molecular interactions, and disease mechanisms. However, its complex data model and keyword-based search interface present accessibility barriers for non-expert users. In contrast, general-purpose conversational AI systems offer intuitive natural language interfaces but lack the domain-specificity, transparent sourcing, and factual reliability required for scientific applications. To address this gap, we developed React-to-Me (https://reactome.org/chat), a domain-specific conversational assistant that enables users to query Reactome using natural language while maintaining scientific rigor and source traceability. React-to-Me integrates hybrid retrieval-augmented generation (RAG) with constrained language model generation to ensure that all responses are grounded in curated Reactome content and directly linked to corresponding knowledgebase entries. When internal coverage is insufficient, the system defers to trusted external biomedical sources rather than generating speculative or unverified content. Computational benchmarking confirmed that combining semantic vector search with keyword-based matching substantially improved contextual grounding and factual precision relative to dense-only retrieval baselines. In blinded expert evaluations, grounded responses were more likely to receive higher quality ratings than ungrounded counterparts, with significant gains in factual accuracy, biological specificity, and mechanistic depth. User surveys further indicated strong satisfaction with ease of use, citation reliability, and factual accuracy. These findings demonstrate that domain-specific grounding can markedly improve the reliability and usability of conversational AI for biological knowledge exploration. React-to-Me provides a transparent and scientifically robust interface for accessing and exploring Reactome content and is freely available at https://reactome.org/chat. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/693752v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1ef0d73org.highwire.dtl.DTLVardef@15c2d4dorg.highwire.dtl.DTLVardef@a2338corg.highwire.dtl.DTLVardef@e639fd_HPS_FORMAT_FIGEXP M_FIG C_FIG

scientific communication and education↗

bioRxiv2021

An Incrementally Updatable and Scalable System for Large-Scale Sequence Search using LSM Trees

MotivationIn the past few years, researchers have proposed numerous indexing schemes for searching large databases of raw sequencing experiments. Most of these proposed indexes are approximate (i.e. with one-sided errors) in order to save space. Recently, researchers have published exact indexes--Mantis, VariMerge, and Bifrost--that can serve as colored de Bruijn graph representations in addition to serving as k-mer indexes. This new type of index is promising because it has the potential to support more complex analyses than simple searches. However, in order to be useful as indexes for large and growing repositories of raw sequencing data, they must scale to thousands of experiments and support efficient insertion of new data. ResultsIn this paper, we show how to build a scalable and updatable exact sequence-search index. Specifically, we extend Mantis using the Bentley-Saxe transformation to support efficient updates. We demonstrate Mantiss scalability by constructing an index of{approx} 40K samples from SRA by adding samples one at a time to an initial index of 10K samples. Compared to VariMerge and Bifrost, Mantis is more efficient in terms of index-construction time and memory, query time and memory, and index size. In our benchmarks, VariMerge and Bifrost scaled to only 5K and 80 samples, respectively, while Mantis scaled to more than 39K samples. Queries were over 24x faster in Mantis than in Bifrost (VariMerge does not immediately support general search queries we require). Mantis indexes were about 2.5x smaller than Bifrosts indexes and about half as big as VariMerges indexes. AvailabilityThe updatable Mantis implementation is available at https://github.com/splatlab/mantis/tree/mergeMSTs. Contactrob@cs.umd.edu Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2020

Puffaligner: An Efficient and Accurate Aligner Based on the Pufferfish Index

MotivationSequence alignment is one of the first steps in many modern genomic analyses, such as variant detection, transcript abundance estimation and metagenomic profiling. Unfortunately, it is often a computationally expensive procedure. As the quantity of data and wealth of different assays and applications continue to grow, the need for accurate and fast alignment tools persists. ResultsIn this paper, we introduce PuffAligner, a fast, accurate and versatile aligner built on top of the Pufferfish index. PuffAligner is able to produce highly-sensitive alignments, similar to those of Bowtie2, but much more quickly. While exhibiting similar speed to the ultrafast STAR aligner, PuffAligner requires considerably less memory to construct its index and align reads. PuffAligner strikes a desirable balance with respect to the time, space, and accuracy tradeoffs made by different alignment tools, and provides a promising foundation on which to test new alignment ideas over large collections of sequences. AvailabilityPuffAligner is a free and open-source software. It is implemented in C++14 and can be obtained from https://github.com/COMBINE-lab/pufferfish/tree/cigar-strings

bioinformatics↗

bioRxiv2018

An Efficient, Scalable and Exact Representation of High-Dimensional Color Information Enabled via de Bruijn Graph Search

The colored de Bruijn graph (cdbg) and its variants have become an important combinatorial structure used in numerous areas in genomics, such as population-level variation detection in metagenomic samples, large scale sequence search, and cdbg-based reference sequence indices. As samples or genomes are added to the cdbg, the color information comes to dominate the space required to represent this data structure.\n\nIn this paper, we show how to represent the color information efficiently by adopting a hierarchical encoding that exploits correlations among color classes -- patterns of color occurrence -- present in the de Bruijn graph (dbg). A major challenge in deriving an efficient encoding of the color information that takes advantage of such correlations is determining which color classes are close to each other in the high-dimensional space of possible color patterns. We demonstrate that the dbg itself can be used as an efficient mechanism to search for approximate nearest neighbors in this space. While our approach reduces the encoding size of the color information even for relatively small cdbgs (hundreds of experiments), the gains are particularly consequential as the number of potential colors (i.e. samples or references) grows to thousands of experiments.\n\nWe apply this encoding in the context of two different applications; the implicit cdbg used for a large-scale sequence search index, Mantis, as well as the encoding of color information used in population-level variation detection by tools such as Vari and Rainbowfish. Our results show significant improvements in the overall size and scalability of representation of the color information. In our experiment on 10,000 samples, we achieved more than 11x better compression compared to RRR.

bioinformatics↗

bioRxiv2017

Mantis: A Fast, Small, and Exact Large-Scale Sequence Search Index

MotivationSequence-level searches on large collections of RNA-seq experiments, such as the NIH Sequence Read Archive (SRA), would enable one to ask many questions about the expression or variation of a given transcript in a population. Bloom filter-based indexes and variants, such as the Sequence Bloom Tree, have been proposed in the past to solve this problem. However, these approaches suffer from fundamental limitations of the Bloom filter, resulting in slow build and query times, less-than-optimal space usage, and large numbers of false positives.\n\nResultsThis paper introduces Mantis, a space-efficient data structure that can be used to index thousands of rawread experiments and facilitate large-scale sequence searches on those experiments. Mantis uses counting quotient filters instead of Bloom filters, enabling rapid index builds and queries, small indexes, and exact results, i.e., no false positives or negatives. Furthermore, Mantis is also a colored de Bruijn graph representation, so it supports fast graph traversal and other topological analyses in addition to large-scale sequence-level searches.\n\nIn our performance evaluation, index construction with Mantis is 4.4x faster and yields a 20% smaller index than the state-of-the-art split sequence Bloom tree (SSBT). For queries, Mantis is 6x -108x faster than SSBT and has no false positives or false negatives. For example, Mantis was able to search for all 200,400 known human transcripts in an index of 2652 human blood, breast, and brain RNA-seq experiments in one hour and 22 minutes; SBT took close to 4 days and AllSomeSBT took about eight hours.\n\nMantis is written in C++11 and is available at https://github.com/splatlab/mantis.

bioinformatics↗

bioRxiv2017

A space and time-efficient index for the compacted colored de Bruijn graph

We present a novel data structure for representing and indexing the compacted colored de Bruijn graph, which allows for efficient pattern matching and retrieval of the reference information associated with each k-mer. As the popularity of the de Bruijn graph as an index has increased over the past few years, so have the number of proposed representations of this structure. Existing structures typically fall into two categories; those that are hashing-based and provide very fast access to the underlying k-mer information, and those that are space-frugal and provide asymptotically efficient but practically slower pattern search.\n\nOur representation achieves a compromise between these two extremes. By building upon minimum perfect hashing, carefully organizing our data structure, and making use of succinct representations where applicable, our data structure provides practically fast k-mer lookup while greatly reducing the space compared to traditional hashing-based implementations. Further, we describe a sampling scheme built on the same underlying representation, which provides the ability to trade off k-mer query speed for a reduction in the de Bruijn graph index size. We believe this representation strikes a desirable balance between speed and space usage, and it will allow for fast search on large reference sequences.\n\nPufferfish is developed in C++11, is open source (GPL v3), and is available at https://github.com/COMBINE-lab/Pufferfish. The scripts used to generate the results in this manuscript are available at https://github.com/COMBINE-lab/pufferfish_experiments.

bioinformatics↗

bioRxiv2017

Rainbowfish: A Succinct Colored de Bruijn Graph Representation

The colored de Bruijn graph-- a variant of the de Bruijn graph which associates each edge (i.e., k-mer) with some set of colors -- is an increasingly important combinatorial structure in computational biology. Iqbal et al. demonstrated the utility of this structure for representing and assembling a collection (pop-ulation) of genomes, and showed how it can be used to accurately detect genetic variants. Muggli et al. introduced VARI, a representation of the colored de Bruijn graph that adopts the BOSS representation for the de Bruijn graph topology and achieves considerable savings in space over Cortex, albeit with some sacrifice in speed. The memory-efficient representation of VARI allows the colored de Bruijn graph to be constructed and analyzed for large datasets, beyond what is possible with Cortex.\n\nIn this paper, we introduce Rainbowfish, a succinct representation of the color information of the colored de Bruijn graph that reduces the space usage even further. Our representation also uses BOSS to represent the de Bruijn graph, but decomposes the color sets based on an equivalence relation and exploits the inherent skewness in the distribution of these color sets. The Rainbowfish representation is compressed based on the 0th-order entropy of the color sets, which can lead to a significant reduction in the space required to store the relevant information for each edge. In practice, Rainbowfish achieves up to a 20 x improvement in space over VARI. Rainbowfish is written in C++11 and is available at https://github.com/COMBINE-lab/rainbowfish.

bioinformatics↗