Biology subjects

Lu, Y. Y.

Publications and source records attributed to Lu, Y. Y..

8 recordsLinked to original sources

bioRxiv2026

Diagnosing protein sequence search in the era of language models

Protein language model (PLM) based search is rapidly emerging as a successor to classical sequence alignment, with recent high-profile studies reporting substantial improvements in speed and remote homology detection. However, success on standard benchmarks does not guarantee that similarity derived from PLM embeddings constitutes reliable biological evidence. Here, we introduce PLM-GUARD, a diagnostic framework designed to interrogate the underlying meaning of protein search scores and assess their biological trustworthiness. PLM-GUARD comprises six sanity checks spanning biological fidelity, semantic validity, and manipulation safety. Across eight representative search methods, classical alignment-based systems demonstrate remarkable robustness, whereas current PLM-based methods fail broadly across all three dimensions. Notably, hybrid methods show intermediate results, indicating that alignment is still critical for ensuring biologically grounded correspondence. Our findings provide a timely clarification for the field and underscore the necessity of diagnostic evaluation as protein search enters the era of language models.

bioinformatics↗

bioRxiv2022

Proteome-wide identification of amino acid substitutions deleterious for protein function

DNA sequencing has led to the discovery of millions of mutations that change the encoded protein sequences, but the impact of nearly all of these mutations on protein function is unknown. We addressed this scarcity of functional data by developing Miro, a proteomic technology that uses mistranslation to introduce amino acid substitutions and biochemical assays to quantify functional differences of thousands of protein variants by mass spectrometry. We apply this technology to the proteome of yeast to reveal amino acid substitutions that impact protein structure, ligand binding, protein-protein interactions, protein post-translational modifications, and protein thermal stability. Adapting Miro to human cells will provide a means to efficiently accelerate our mechanistic interpretation of genomic mutations to predict disease risk.

genomics↗

bioRxiv2021

ACE: Explaining cluster from an adversarial perspective

A common workflow in single-cell RNA-seq analysis is to project the data to a latent space, cluster the cells in that space, and identify sets of marker genes that explain the differences among the discovered clusters. A primary drawback to this three-step procedure is that each step is carried out independently, thereby neglecting the effects of the nonlinear embedding and inter-gene dependencies on the selection of marker genes. Here we propose an integrated deep learning framework, Adversarial Clustering Explanation (ACE), that bundles all three steps into a single work-flow. The method thus moves away from the notion of "marker genes" to instead identify a panel of explanatory genes. This panel may include genes that are not only enriched but also depleted relative to other cell types, as well as genes that exhibit differences between closely related cell types. Empirically, we demonstrate that ACE is able to identify gene panels that are both highly discriminative and nonredundant, and we demonstrate the applicability of ACE to an image recognition task. 1

bioinformatics↗

bioRxiv2021

DIAmeter: Matching peptides to data-independent acquisition mass spectrometry data

Tandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a "spectral library"), but this approach is expensive because the libraries do not typically generalize well across laboratories. Here we propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Unlike other library-free DIA analysis methods, DIAmeter supports data generated using both wide and narrow isolation windows, can readily detect peptides containing post-translational modifications, can analyze data from a variety of instrument platforms, and is capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan.

bioinformatics↗

bioRxiv2021

SAINT: automatic taxonomy embedding and categorization by Siamese triplet network

MotivationUnderstanding the phylogenetic relationship among organisms is the key in contemporary evolutionary study and sequence analysis is the workhorse towards this goal. Conventional approaches to sequence analysis are based on sequence alignment, which is neither scalable to large-scale datasets due to computational inefficiency nor adaptive to next-generation sequencing (NGS) data. Alignment-free approaches are typically used as computationally effective alternatives yet still suffering the high demand of memory consumption. One desirable sequence comparison method at large-scale requires succinctly-organized sequence data management, as well as prompt sequence retrieval given a never-before-seen sequence as query. ResultsIn this paper, we proposed a novel approach, referred to as SAINT, for efficient and accurate alignment-free sequence comparison. Compared to existing alignment-free sequence comparison methods, SAINT offers advantages in two aspects: (1) SAINT is a weakly-supervised learning method where the embedding function is learned automatically from the easily-acquired data; (2) SAINT utilizes the non-linear deep learning-based model which potentially better captures the complicated relationship among genome sequences. We have applied SAINT to real-world datasets to demonstrate its empirical utility, both qualitatively and quantitatively. Considering the extensive applicability of alignment-free sequence comparison methods, we expect SAINT to motivate a more extensive set of applications in sequence comparison at large scale. AvailabilityThe open source, Apache licensed, python-implemented code will be available upon acceptance. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Ledidi: Designing genome edits that induce functional activity

The development of modern genome editing tools has enabled researchers to make edits with high precision, but has left unsolved the problem of designing these edits. We propose Ledidi, an approach that treats the design of genomic edits as an optimization problem where the goal is to produce the desired output from a predictive model. The discrete nature of biological sequences makes direct optimization challenging, but we overcome this by using the Gumbel-Softmax reparameterization trick. We validate Ledidi by pairing it with the Basenji model, which makes predictions for thousands of functional profiles, and designing edits that affect CTCF binding and induce cell type-specific binding of JUND.

bioinformatics↗

bioRxiv2018

Predicting gene expression in the human malaria parasite Plasmodium falciparum

Empirical evidence suggests that the malaria parasite Plasmodium falciparum employs a broad range of mechanisms to regulate gene transcription throughout the organisms complex life cycle. To better understand this regulatory machinery, we assembled a rich collection of genomic and epigenomic data sets, including information about transcription factor (TF) binding motifs, patterns of covalent histone modifications, nucleosome occupancy, GC content, and global 3D genome architecture. We used these data to train machine learning models to discriminate between high-expression and low-expression genes, focusing on three distinct stages of the red blood cell phase of the Plasmodium life cycle. Our results highlight the importance of histone modifications and 3D chromatin architecture and suggest a relatively small role for TF binding in Plasmodium transcriptional regulation.

genomics↗

bioRxiv2018

Joint precursor elution profile inference via regression for peptide detection in data-independent acquisition mass spectra

In data independent acquisition (DIA) mass spectrometry, precursor scans are interleaved with wide-window fragmentation scans, resulting in complex fragmentation spectra containing multiple co-eluting peptide species. In this setting, detecting the isotope distribution profiles of intact peptides in the precursor scans can be a critical initial step in accurate peptide detection and quantification. This peak detection step is particularly challenging when the isotope peaks associated with two different peptide species overlap--or interfere--with one another. We propose a regression model, called Siren, to detect isotopic peaks in precursor DIA data that can explicitly account for interference. We validate Sirens peak-calling performance on a variety of data sets by counting how many of the peaks Siren identifies are associated with confidently detected peptides. In particular, we demonstrate that substituting the Siren regression model in place of the existing peak-calling step in DIA-Umpire leads to improved overall rates of peptide detection.

bioinformatics↗