bluuebunny/crossref_metadata_2025 huggingface dataset:
CellBRF: a feature selection method for single-cell clustering using cell balance and random forest
Authors: Xu Yunpei , Li Hong-Dong , Lin Cui-Xiang , Zheng Ruiqing , Li Yaohang , Xu Jinhui , Wang Jianxin
Published: June 2023

Motivation

Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect the complexity of biological tissues through cell sub-population identification in combination with clustering approaches. Feature selection is a critical step for improving the accuracy and interpretability of single-cell clustering. Existing feature selection methods underutilize the discriminatory potential of genes across distinct cell types. We hypothesize that incorporating such information could further boost the performance of single cell clustering.

Results

We develop CellBRF, a feature selection method that considers genes’ relevance to cell types for single-cell clustering. The key idea is to identify genes that are most important for discriminating cell types through random forests guided by predicted cell labels. Moreover, it proposes a class balancing strategy to mitigate the impact of unbalanced cell type distributions on feature importance evaluation. We benchmark CellBRF on 33 scRNA-seq datasets representing diverse biological scenarios and demonstrate that it substantially outperforms state-of-the-art feature selection methods in terms of clustering accuracy and cell neighborhood consistency. Furthermore, we demonstrate the outstanding performance of our selected features through three case studies on cell differentiation stage identification, non-malignant cell subtype identification, and rare cell identification. CellBRF provides a new and effective tool to boost single-cell clustering accuracy.

Availability and implementation

All source codes of CellBRF are freely available at https://github.com/xuyp-csu/CellBRF.
ChromDL: a next-generation regulatory DNA classifier
Authors: Hill Christopher , Hudaiberdiev Sanjarbek , Ovcharenko Ivan
Published: June 2023

Motivation

Predicting the regulatory function of non-coding DNA using only the DNA sequence continues to be a major challenge in genomics. With the advent of improved optimization algorithms, faster GPU speeds, and more intricate machine-learning libraries, hybrid convolutional and recurrent neural network architectures can be constructed and applied to extract crucial information from non-coding DNA.

Results

Using a comparative analysis of the performance of thousands of Deep Learning architectures, we developed ChromDL, a neural network architecture combining bidirectional gated recurrent units, convolutional neural networks, and bidirectional long short-term memory units, which significantly improves upon a range of prediction metrics compared to its predecessors in transcription factor binding site, histone modification, and DNase-I hyper-sensitive site detection. Combined with a secondary model, it can be utilized for accurate classification of gene regulatory elements. The model can also detect weak transcription factor binding as compared to previously developed methods and has the potential to help delineate transcription factor binding motif specificities.

Availability and implementation

The ChromDL source code can be found at https://github.com/chrishil1/ChromDL.
Seeding with minimized subsequence
Authors: Li Xiang , Shi Qian , Chen Ke , Shao Mingfu
Published: June 2023

Motivation

Modern methods for computation-intensive tasks in sequence analysis (e.g. read mapping, sequence alignment, genome assembly, etc.) often first transform each sequence into a list of short, regular-length seeds so that compact data structures and efficient algorithms can be employed to handle the ever-growing large-scale data. Seeding methods using kmers (substrings of length k) have gained tremendous success in processing sequencing data with low mutation/error rates. However, they are much less effective for sequencing data with high error rates as kmers cannot tolerate errors.

Results

We propose SubseqHash, a strategy that uses subsequences, rather than substrings, as seeds. Formally, SubseqHash maps a string of length n to its smallest subsequence of length k, k < n, according to a given order overall length-k strings. Finding the smallest subsequence of a string by enumeration is impractical as the number of subsequences grows exponentially. To overcome this barrier, we propose a novel algorithmic framework that consists of a specifically designed order (termed ABC order) and an algorithm that computes the minimized subsequence under an ABC order in polynomial time. We first show that the ABC order exhibits the desired property and the probability of hash collision using the ABC order is close to the Jaccard index. We then show that SubseqHash overwhelmingly outperforms the substring-based seeding methods in producing high-quality seed-matches for three critical applications: read mapping, sequence alignment, and overlap detection. SubseqHash presents a major algorithmic breakthrough for tackling the high error rates and we expect it to be widely adapted for long-reads analysis.

Availability and implementation

SubseqHash is freely available at https://github.com/Shao-Group/subseqhash.
Deep statistical modelling of nanopore sequencing translocation times reveals latent non-B DNA structures
Authors: Hosseini Marjan , Palmer Aaron , Manka William , Grady Patrick G S , Patchigolla Venkata , Bi Jinbo , O’Neill Rachel J , Chi Zhiyi , Aguiar Derek
Published: June 2023

Motivation

Non-canonical (or non-B) DNA are genomic regions whose three-dimensional conformation deviates from the canonical double helix. Non-B DNA play an important role in basic cellular processes and are associated with genomic instability, gene regulation, and oncogenesis. Experimental methods are low-throughput and can detect only a limited set of non-B DNA structures, while computational methods rely on non-B DNA base motifs, which are necessary but not sufficient indicators of non-B structures. Oxford Nanopore sequencing is an efficient and low-cost platform, but it is currently unknown whether nanopore reads can be used for identifying non-B structures.

Results

We build the first computational pipeline to predict non-B DNA structures from nanopore sequencing. We formalize non-B detection as a novelty detection problem and develop the GoFAE-DND, an autoencoder that uses goodness-of-fit (GoF) tests as a regularizer. A discriminative loss encourages non-B DNA to be poorly reconstructed and optimizing Gaussian GoF tests allows for the computation of P-values that indicate non-B structures. Based on whole genome nanopore sequencing of NA12878, we show that there exist significant differences between the timing of DNA translocation for non-B DNA bases compared with B-DNA. We demonstrate the efficacy of our approach through comparisons with novelty detection methods using experimental data and data synthesized from a new translocation time simulator. Experimental validations suggest that reliable detection of non-B DNA from nanopore sequencing is achievable.

Availability and implementation

Source code is available at https://github.com/bayesomicslab/ONT-nonb-GoFAE-DND.
Phylogenomic branch length estimation using quartets
Authors: Tabatabaee Yasamin , Zhang Chao , Warnow Tandy , Mirarab Siavash
Published: June 2023

Motivation

Branch lengths and topology of a species tree are essential in most downstream analyses, including estimation of diversification dates, characterization of selection, understanding adaptation, and comparative genomics. Modern phylogenomic analyses often use methods that account for the heterogeneity of evolutionary histories across the genome due to processes such as incomplete lineage sorting. However, these methods typically do not generate branch lengths in units that are usable by downstream applications, forcing phylogenomic analyses to resort to alternative shortcuts such as estimating branch lengths by concatenating gene alignments into a supermatrix. Yet, concatenation and other available approaches for estimating branch lengths fail to address heterogeneity across the genome.

Results

In this article, we derive expected values of gene tree branch lengths in substitution units under an extension of the multispecies coalescent (MSC) model that allows substitutions with varying rates across the species tree. We present CASTLES, a new technique for estimating branch lengths on the species tree from estimated gene trees that uses these expected values, and our study shows that CASTLES improves on the most accurate prior methods with respect to both speed and accuracy.

Availability and implementation

CASTLES is available at https://github.com/ytabatabaee/CASTLES.
CProMG: controllable protein-oriented molecule generation with desired binding affinity and drug-like properties
Authors: Li Jia-Ning , Yang Guang , Zhao Peng-Cheng , Wei Xue-Xin , Shi Jian-Yu
Published: June 2023

Motivation

Deep learning-based molecule generation becomes a new paradigm of de novo molecule design since it enables fast and directional exploration in the vast chemical space. However, it is still an open issue to generate molecules, which bind to specific proteins with high-binding affinities while owning desired drug-like physicochemical properties.

Results

To address these issues, we elaborate a novel framework for controllable protein-oriented molecule generation, named CProMG, which contains a 3D protein embedding module, a dual-view protein encoder, a molecule embedding module, and a novel drug-like molecule decoder. Based on fusing the hierarchical views of proteins, it enhances the representation of protein binding pockets significantly by associating amino acid residues with their comprising atoms. Through jointly embedding molecule sequences, their drug-like properties, and binding affinities w.r.t. proteins, it autoregressively generates novel molecules having specific properties in a controllable manner by measuring the proximity of molecule tokens to protein residues and atoms. The comparison with state-of-the-art deep generative methods demonstrates the superiority of our CProMG. Furthermore, the progressive control of properties demonstrates the effectiveness of CProMG when controlling binding affinity and drug-like properties. After that, the ablation studies reveal how its crucial components contribute to the model respectively, including hierarchical protein views, Laplacian position encoding as well as property control. Last, a case study w.r.t. protein illustrates the novelty of CProMG and the ability to capture crucial interactions between protein pockets and molecules. It’s anticipated that this work can boost de novo molecule design.

Availability and implementation

The code and data underlying this article are freely available at https://github.com/lijianing0902/CProMG.
A weighted distance-based approach for deriving consensus tumor evolutionary trees
Authors: Guang Ziyun , Smith-Erb Matthew , Oesper Layla
Published: June 2023

Motivation

The acquisition of somatic mutations by a tumor can be modeled by a type of evolutionary tree. However, it is impossible to observe this tree directly. Instead, numerous algorithms have been developed to infer such a tree from different types of sequencing data. But such methods can produce conflicting trees for the same patient, making it desirable to have approaches that can combine several such tumor trees into a consensus or summary tree. We introduce The Weighted m-Tumor Tree Consensus Problem (W-m-TTCP) to find a consensus tree among multiple plausible tumor evolutionary histories, each assigned a confidence weight, given a specific distance measure between tumor trees. We present an algorithm called TuELiP that is based on integer linear programming which solves the W-m-TTCP, and unlike other existing consensus methods, allows the input trees to be weighted differently.

Results

On simulated data we show that TuELiP outperforms two existing methods at correctly identifying the true underlying tree used to create the simulations. We also show that the incorporation of weights can lead to more accurate tree inference. On a Triple-Negative Breast Cancer dataset, we show that including confidence weights can have important impacts on the consensus tree identified.

Availability

An implementation of TuELiP and simulated datasets are available at https://bitbucket.org/oesperlab/consensus-ilp/src/main/.
Deep Local Analysis deconstructs protein–protein interfaces and accurately estimates binding affinity changes upon mutation
Authors: Mohseni Behbahani Yasser , Laine Elodie , Carbone Alessandra
Published: June 2023

Motivation

The spectacular recent advances in protein and protein complex structure prediction hold promise for reconstructing interactomes at large-scale and residue resolution. Beyond determining the 3D arrangement of interacting partners, modeling approaches should be able to unravel the impact of sequence variations on the strength of the association.

Results

In this work, we report on Deep Local Analysis, a novel and efficient deep learning framework that relies on a strikingly simple deconstruction of protein interfaces into small locally oriented residue-centered cubes and on 3D convolutions recognizing patterns within cubes. Merely based on the two cubes associated with the wild-type and the mutant residues, DLA accurately estimates the binding affinity change for the associated complexes. It achieves a Pearson correlation coefficient of 0.735 on about 400 mutations on unseen complexes. Its generalization capability on blind datasets of complexes is higher than the state-of-the-art methods. We show that taking into account the evolutionary constraints on residues contributes to predictions. We also discuss the influence of conformational variability on performance. Beyond the predictive power on the effects of mutations, DLA is a general framework for transferring the knowledge gained from the available non-redundant set of complex protein structures to various tasks. For instance, given a single partially masked cube, it recovers the identity and physicochemical class of the central residue. Given an ensemble of cubes representing an interface, it predicts the function of the complex.

Availability and implementation

Source code and models are available at http://gitlab.lcqb.upmc.fr/DLA/DLA.git.
AttOmics: attention-based architecture for diagnosis and prognosis from omics data
Authors: Beaude Aurélien , Rafiee Vahid Milad , Augé Franck , Zehraoui Farida , Hanczar Blaise
Published: June 2023

Motivation

The increasing availability of high-throughput omics data allows for considering a new medicine centered on individual patients. Precision medicine relies on exploiting these high-throughput data with machine-learning models, especially the ones based on deep-learning approaches, to improve diagnosis. Due to the high-dimensional small-sample nature of omics data, current deep-learning models end up with many parameters and have to be fitted with a limited training set. Furthermore, interactions between molecular entities inside an omics profile are not patient specific but are the same for all patients.

Results

In this article, we propose AttOmics, a new deep-learning architecture based on the self-attention mechanism. First, we decompose each omics profile into a set of groups, where each group contains related features. Then, by applying the self-attention mechanism to the set of groups, we can capture the different interactions specific to a patient. The results of different experiments carried out in this article show that our model can accurately predict the phenotype of a patient with fewer parameters than deep neural networks. Visualizing the attention maps can provide new insights into the essential groups for a particular phenotype.

Availability and implementation

The code and data are available at https://forge.ibisc.univ-evry.fr/abeaude/AttOmics. TCGA data can be downloaded from the Genomic Data Commons Data Portal.
Themisto: a scalable colored k-mer index for sensitive pseudoalignment against hundreds of thousands of bacterial genomes
Authors: Alanko Jarno N , Vuohtoniemi Jaakko , Mäklin Tommi , Puglisi Simon J
Published: June 2023

Motivation

Huge datasets containing whole-genome sequences of bacterial strains are now commonplace and represent a rich and important resource for modern genomic epidemiology and metagenomics. In order to efficiently make use of these datasets, efficient indexing data structures—that are both scalable and provide rapid query throughput—are paramount.

Results

Here, we present Themisto, a scalable colored k-mer index designed for large collections of microbial reference genomes, that works for both short and long read data. Themisto indexes 179 thousand Salmonella enterica genomes in 9 h. The resulting index takes 142 gigabytes. In comparison, the best competing tools Metagraph and Bifrost were only able to index 11 000 genomes in the same time. In pseudoalignment, these other tools were either an order of magnitude slower than Themisto, or used an order of magnitude more memory. Themisto also offers superior pseudoalignment quality, achieving a higher recall than previous methods on Nanopore read sets.

Availability and implementation

Themisto is available and documented as a C++ package at https://github.com/algbio/themisto available under the GPLv2 license.
AdenPredictor: accurate prediction of the adenylation domain specificity of nonribosomal peptide biosynthetic gene clusters in microbial genomes
Authors: Mongia Mihir , Baral Romel , Adduri Abhinav , Yan Donghui , Liu Yudong , Bian Yuying , Kim Paul , Behsaz Bahar , Mohimani Hosein
Published: June 2023

SummaryMicrobial natural products represent a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class that include antibiotics, immunosuppressants, anticancer agents, toxins, siderophores, pigments, and cytostatics. The discovery of novel NRPs remains a laborious process because many NRPs consist of nonstandard amino acids that are assembled by nonribosomal peptide synthetases (NRPSs). Adenylation domains (A-domains) in NRPSs are responsible for selection and activation of monomers appearing in NRPs. During the past decade, several support vector machine-based algorithms have been developed for predicting the specificity of the monomers present in NRPs. These algorithms utilize physiochemical features of the amino acids present in the A-domains of NRPSs. In this article, we benchmarked the performance of various machine learning algorithms and features for predicting specificities of NRPSs and we showed that the extra trees model paired with one-hot encoding features outperforms the existing approaches. Moreover, we show that unsupervised clustering of 453 560 A-domains reveals many clusters that correspond to potentially novel amino acids. While it is challenging to predict the chemical structure of these amino acids, we developed novel techniques to predict their various properties, including polarity, hydrophobicity, charge, and presence of aromatic rings, carboxyl, and hydroxyl groups.
Supervised biological network alignment with graph neural networks
Authors: Ding Kerr , Wang Sheng , Luo Yunan
Published: June 2023

Motivation

Despite the advances in sequencing technology, massive proteins with known sequences remain functionally unannotated. Biological network alignment (NA), which aims to find the node correspondence between species’ protein–protein interaction (PPI) networks, has been a popular strategy to uncover missing annotations by transferring functional knowledge across species. Traditional NA methods assumed that topologically similar proteins in PPIs are functionally similar. However, it was recently reported that functionally unrelated proteins can be as topologically similar as functionally related pairs, and a new data-driven or supervised NA paradigm has been proposed, which uses protein function data to discern which topological features correspond to functional relatedness.

Results

Here, we propose GraNA, a deep learning framework for the supervised NA paradigm for the pairwise NA problem. Employing graph neural networks, GraNA utilizes within-network interactions and across-network anchor links for learning protein representations and predicting functional correspondence between across-species proteins. A major strength of GraNA is its flexibility to integrate multi-faceted non-functional relationship data, such as sequence similarity and ortholog relationships, as anchor links to guide the mapping of functionally related proteins across species. Evaluating GraNA on a benchmark dataset composed of several NA tasks between different pairs of species, we observed that GraNA accurately predicted the functional relatedness of proteins and robustly transferred functional annotations across species, outperforming a number of existing NA methods. When applied to a case study on a humanized yeast network, GraNA also successfully discovered functionally replaceable human–yeast protein pairs that were documented in previous studies.

Availability and implementation

The code of GraNA is available at https://github.com/luo-group/GraNA.
DeepCoVDR: deep transfer learning with graph transformer and cross-attention for predicting COVID-19 drug response
Authors: Huang Zhijian , Zhang Pan , Deng Lei
Published: June 2023

Motivation

The coronavirus disease 2019 (COVID-19) remains a global public health emergency. Although people, especially those with underlying health conditions, could benefit from several approved COVID-19 therapeutics, the development of effective antiviral COVID-19 drugs is still a very urgent problem. Accurate and robust drug response prediction to a new chemical compound is critical for discovering safe and effective COVID-19 therapeutics.

Results

In this study, we propose DeepCoVDR, a novel COVID-19 drug response prediction method based on deep transfer learning with graph transformer and cross-attention. First, we adopt a graph transformer and feed-forward neural network to mine the drug and cell line information. Then, we use a cross-attention module that calculates the interaction between the drug and cell line. After that, DeepCoVDR combines drug and cell line representation and their interaction features to predict drug response. To solve the problem of SARS-CoV-2 data scarcity, we apply transfer learning and use the SARS-CoV-2 dataset to fine-tune the model pretrained on the cancer dataset. The experiments of regression and classification show that DeepCoVDR outperforms baseline methods. We also evaluate DeepCoVDR on the cancer dataset, and the results indicate that our approach has high performance compared with other state-of-the-art methods. Moreover, we use DeepCoVDR to predict COVID-19 drugs from FDA-approved drugs and demonstrate the effectiveness of DeepCoVDR in identifying novel COVID-19 drugs.

Availability and implementation

https://github.com/Hhhzj-7/DeepCoVDR.
Cell type matching across species using protein embeddings and transfer learning
Authors: Biharie Kirti , Michielsen Lieke , Reinders Marcel J T , Mahfouz Ahmed
Published: June 2023

Motivation

Knowing the relation between cell types is crucial for translating experimental results from mice to humans. Establishing cell type matches, however, is hindered by the biological differences between the species. A substantial amount of evolutionary information between genes that could be used to align the species is discarded by most of the current methods since they only use one-to-one orthologous genes. Some methods try to retain the information by explicitly including the relation between genes, however, not without caveats.

Results

In this work, we present a model to transfer and align cell types in cross-species analysis (TACTiCS). First, TACTiCS uses a natural language processing model to match genes using their protein sequences. Next, TACTiCS employs a neural network to classify cell types within a species. Afterward, TACTiCS uses transfer learning to propagate cell type labels between species. We applied TACTiCS on scRNA-seq data of the primary motor cortex of human, mouse, and marmoset. Our model can accurately match and align cell types on these datasets. Moreover, our model outperforms Seurat and the state-of-the-art method SAMap. Finally, we show that our gene matching method results in better cell type matches than BLAST in our model.

Availability and implementation

The implementation is available on GitHub (https://github.com/kbiharie/TACTiCS). The preprocessed datasets and trained models can be downloaded from Zenodo (https://doi.org/10.5281/zenodo.7582460).
PlasBin-flow: a flow-based MILP algorithm for plasmid contigs binning
Authors: Mane Aniket , Faizrahnemoon Mahsa , Vinař Tomáš , Brejová Broňa , Chauve Cedric
Published: June 2023

Motivation

The analysis of bacterial isolates to detect plasmids is important due to their role in the propagation of antimicrobial resistance. In short-read sequence assemblies, both plasmids and bacterial chromosomes are typically split into several contigs of various lengths, making identification of plasmids a challenging problem. In plasmid contig binning, the goal is to distinguish short-read assembly contigs based on their origin into plasmid and chromosomal contigs and subsequently sort plasmid contigs into bins, each bin corresponding to a single plasmid. Previous works on this problem consist of de novo approaches and reference-based approaches. De novo methods rely on contig features such as length, circularity, read coverage, or GC content. Reference-based approaches compare contigs to databases of known plasmids or plasmid markers from finished bacterial genomes.

Results

Recent developments suggest that leveraging information contained in the assembly graph improves the accuracy of plasmid binning. We present PlasBin-flow, a hybrid method that defines contig bins as subgraphs of the assembly graph. PlasBin-flow identifies such plasmid subgraphs through a mixed integer linear programming model that relies on the concept of network flow to account for sequencing coverage, while also accounting for the presence of plasmid genes and the GC content that often distinguishes plasmids from chromosomes. We demonstrate the performance of PlasBin-flow on a real dataset of bacterial samples.

Availability and implementation

https://github.com/cchauve/PlasBin-flow.
KR4SL: knowledge graph reasoning for explainable prediction of synthetic lethality
Authors: Zhang Ke , Wu Min , Liu Yong , Feng Yimiao , Zheng Jie
Published: June 2023

Motivation

Synthetic lethality (SL) is a promising strategy for anticancer therapy, as inhibiting SL partners of genes with cancer-specific mutations can selectively kill the cancer cells without harming the normal cells. Wet-lab techniques for SL screening have issues like high cost and off-target effects. Computational methods can help address these issues. Previous machine learning methods leverage known SL pairs, and the use of knowledge graphs (KGs) can significantly enhance the prediction performance. However, the subgraph structures of KG have not been fully explored. Besides, most machine learning methods lack interpretability, which is an obstacle for wide applications of machine learning to SL identification.

Results

We present a model named KR4SL to predict SL partners for a given primary gene. It captures the structural semantics of a KG by efficiently constructing and learning from relational digraphs in the KG. To encode the semantic information of the relational digraphs, we fuse textual semantics of entities into propagated messages and enhance the sequential semantics of paths using a recurrent neural network. Moreover, we design an attentive aggregator to identify critical subgraph structures that contribute the most to the SL prediction as explanations. Extensive experiments under different settings show that KR4SL significantly outperforms all the baselines. The explanatory subgraphs for the predicted gene pairs can unveil prediction process and mechanisms underlying synthetic lethality. The improved predictive power and interpretability indicate that deep learning is practically useful for SL-based cancer drug target discovery.

Availability and implementation

The source code is freely available at https://github.com/JieZheng-ShanghaiTech/KR4SL.
Trap spaces of multi-valued networks: definition, computation, and applications
Authors: Trinh Van-Giang , Benhamou Belaid , Henzinger Thomas , Pastva Samuel
Published: June 2023

Motivation

Boolean networks are simple but efficient mathematical formalism for modelling complex biological systems. However, having only two levels of activation is sometimes not enough to fully capture the dynamics of real-world biological systems. Hence, the need for multi-valued networks (MVNs), a generalization of Boolean networks. Despite the importance of MVNs for modelling biological systems, only limited progress has been made on developing theories, analysis methods, and tools that can support them. In particular, the recent use of trap spaces in Boolean networks made a great impact on the field of systems biology, but there has been no similar concept defined and studied for MVNs to date.

Results

In this work, we generalize the concept of trap spaces in Boolean networks to that in MVNs. We then develop the theory and the analysis methods for trap spaces in MVNs. In particular, we implement all proposed methods in a Python package called trapmvn. Not only showing the applicability of our approach via a realistic case study, we also evaluate the time efficiency of the method on a large collection of real-world models. The experimental results confirm the time efficiency, which we believe enables more accurate analysis on larger and more complex multi-valued models.

Availability and implementation

Source code and data are freely available at https://github.com/giang-trinh/trap-mvn.
Phylogenetic diversity statistics for all clades in a phylogeny
Authors: Grover Siddhant , Markin Alexey , Anderson Tavis K , Eulenstein Oliver
Published: June 2023

The classic quantitative measure of phylogenetic diversity (PD) has been used to address problems in conservation biology, microbial ecology, and evolutionary biology. PD is the minimum total length of the branches in a phylogeny required to cover a specified set of taxa on the phylogeny. A general goal in the application of PD has been identifying a set of taxa of size k that maximize PD on a given phylogeny; this has been mirrored in active research to develop efficient algorithms for the problem. Other descriptive statistics, such as the minimum PD, average PD, and standard deviation of PD, can provide invaluable insight into the distribution of PD across a phylogeny (relative to a fixed value of k). However, there has been limited or no research on computing these statistics, especially when required for each clade in a phylogeny, enabling direct comparisons of PD between clades. We introduce efficient algorithms for computing PD and the associated descriptive statistics for a given phylogeny and each of its clades. In simulation studies, we demonstrate the ability of our algorithms to analyze large-scale phylogenies with applications in ecology and evolutionary biology. The software is available at https://github.com/flu-crew/PD_stats.
isONform: reference-free transcriptome reconstruction from Oxford Nanopore data
Authors: Petri Alexander J , Sahlin Kristoffer
Published: June 2023

Motivation

With advances in long-read transcriptome sequencing, we can now fully sequence transcripts, which greatly improves our ability to study transcription processes. A popular long-read transcriptome sequencing technique is Oxford Nanopore Technologies (ONT), which through its cost-effective sequencing and high throughput, has the potential to characterize the transcriptome in a cell. However, due to transcript variability and sequencing errors, long cDNA reads need substantial bioinformatic processing to produce a set of isoform predictions from the reads. Several genome and annotation-based methods exist to produce transcript predictions. However, such methods require high-quality genomes and annotations and are limited by the accuracy of long-read splice aligners. In addition, gene families with high heterogeneity may not be well represented by a reference genome and would benefit from reference-free analysis. Reference-free methods to predict transcripts from ONT, such as RATTLE, exist, but their sensitivity is not comparable to reference-based approaches.

Results

We present isONform, a high-sensitivity algorithm to construct isoforms from ONT cDNA sequencing data. The algorithm is based on iterative bubble popping on gene graphs built from fuzzy seeds from the reads. Using simulated, synthetic, and biological ONT cDNA data, we show that isONform has substantially higher sensitivity than RATTLE albeit with some loss in precision. On biological data, we show that isONform’s predictions have substantially higher consistency with the annotation-based method StringTie2 compared with RATTLE. We believe isONform can be used both for isoform construction for organisms without well-annotated genomes and as an orthogonal method to verify predictions of reference-based methods.

Availability and implementation

https://github.com/aljpetri/isONform
scKINETICS: inference of regulatory velocity with single-cell transcriptomics data
Authors: Burdziak Cassandra , Zhao Chujun Julia , Haviv Doron , Alonso-Curbelo Direna , Lowe Scott W , Pe’er Dana
Published: June 2023

Motivation

Transcriptional dynamics are governed by the action of regulatory proteins and are fundamental to systems ranging from normal development to disease. RNA velocity methods for tracking phenotypic dynamics ignore information on the regulatory drivers of gene expression variability through time.

Results

We introduce scKINETICS (Key regulatory Interaction NETwork for Inferring Cell Speed), a dynamical model of gene expression change which is fit with the simultaneous learning of per-cell transcriptional velocities and a governing gene regulatory network. Fitting is accomplished through an expectation–maximization approach designed to learn the impact of each regulator on its target genes, leveraging biologically motivated priors from epigenetic data, gene–gene coexpression, and constraints on cells’ future states imposed by the phenotypic manifold. Applying this approach to an acute pancreatitis dataset recapitulates a well-studied axis of acinar-to-ductal transdifferentiation whilst proposing novel regulators of this process, including factors with previously appreciated roles in driving pancreatic tumorigenesis. In benchmarking experiments, we show that scKINETICS successfully extends and improves existing velocity approaches to generate interpretable, mechanistic models of gene regulatory dynamics.

Availability and implementation

All python code and an accompanying Jupyter notebook with demonstrations are available at http://github.com/dpeerlab/scKINETICS.
A multilocus approach for accurate variant calling in low-copy repeats using whole-genome sequencing
Authors: Prodanov Timofey , Bansal Vikas
Published: June 2023

Motivation

Low-copy repeats (LCRs) or segmental duplications are long segments of duplicated DNA that cover > 5% of the human genome. Existing tools for variant calling using short reads exhibit low accuracy in LCRs due to ambiguity in read mapping and extensive copy number variation. Variants in more than 150 genes overlapping LCRs are associated with risk for human diseases.

Methods

We describe a short-read variant calling method, ParascopyVC, that performs variant calling jointly across all repeat copies and utilizes reads independent of mapping quality in LCRs. To identify candidate variants, ParascopyVC aggregates reads mapped to different repeat copies and performs polyploid variant calling. Subsequently, paralogous sequence variants that can differentiate repeat copies are identified using population data and used for estimating the genotype of variants for each repeat copy.

Results

On simulated whole-genome sequence data, ParascopyVC achieved higher precision (0.997) and recall (0.807) than three state-of-the-art variant callers (best precision = 0.956 for DeepVariant and best recall = 0.738 for GATK) in 167 LCR regions. Benchmarking of ParascopyVC using the genome-in-a-bottle high-confidence variant calls for HG002 genome showed that it achieved a very high precision of 0.991 and a high recall of 0.909 across LCR regions, significantly better than FreeBayes (precision = 0.954 and recall = 0.822), GATK (precision = 0.888 and recall = 0.873) and DeepVariant (precision = 0.983 and recall = 0.861). ParascopyVC demonstrated a consistently higher accuracy (mean F1 = 0.947) than other callers (best F1 = 0.908) across seven human genomes.

Availability and implementation

ParascopyVC is implemented in Python and is freely available at https://github.com/tprodanov/ParascopyVC.
CLARIFY: cell–cell interaction and gene regulatory network refinement from spatially resolved transcriptomics
Authors: Bafna Mihir , Li Hechen , Zhang Xiuwei
Published: June 2023

Motivation

Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell–cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model.

Results

We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks.

Availability and implementation

The source code and data is available at https://github.com/MihirBafna/CLARIFY.
Optimal adjustment sets for causal query estimation in partially observed biomolecular networks
Authors: Mohammad-Taheri Sara , Tewari Vartika , Kapre Rohan , Rahiminasab Ehsan , Sachs Karen , Tapley Hoyt Charles , Zucker Jeremy , Vitek Olga
Published: June 2023

Causal query estimation in biomolecular networks commonly selects a ‘valid adjustment set’, i.e. a subset of network variables that eliminates the bias of the estimator. A same query may have multiple valid adjustment sets, each with a different variance. When networks are partially observed, current methods use graph-based criteria to find an adjustment set that minimizes asymptotic variance. Unfortunately, many models that share the same graph topology, and therefore same functional dependencies, may differ in the processes that generate the observational data. In these cases, the topology-based criteria fail to distinguish the variances of the adjustment sets. This deficiency can lead to sub-optimal adjustment sets, and to miss-characterization of the effect of the intervention. We propose an approach for deriving ‘optimal adjustment sets’ that takes into account the nature of the data, bias and finite-sample variance of the estimator, and cost. It empirically learns the data generating processes from historical experimental data, and characterizes the properties of the estimators by simulation. We demonstrate the utility of the proposed approach in four biomolecular Case studies with different topologies and different data generation processes. The implementation and reproducible Case studies are at https://github.com/srtaheri/OptimalAdjustmentSet.
An intrinsically interpretable neural network architecture for sequence-to-function learning
Authors: Balcı Ali Tuğrul , Ebeid Mark Maher , Benos Panayiotis V , Kostka Dennis , Chikina Maria
Published: June 2023

Motivation

Sequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post hoc analyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called totally interpretable sequence-to-function model (tiSFM). tiSFM improves upon the performance of standard multilayer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multilayer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs.

Results

We analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state-of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context-specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFM’s model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition.

Availability and implementation

The source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv, implemented in Python.
Chromothripsis detection with multiple myeloma patients based on deep graph learning
Authors: Yu Jixiang , Chen Nanjun , Zheng Zetian , Gao Ming , Liang Ning , Wong Ka-Chun
Published: June 2023

Motivation

Chromothripsis, associated with poor clinical outcomes, is prognostically vital in multiple myeloma. The catastrophic event is reported to be detectable prior to the progression of multiple myeloma. As a result, chromothripsis detection can contribute to risk estimation and early treatment guidelines for multiple myeloma patients. However, manual diagnosis remains the gold standard approach to detect chromothripsis events with the whole-genome sequencing technology to retrieve both copy number variation (CNV) and structural variation data. Meanwhile, CNV data are much easier to obtain than structural variation data. Hence, in order to reduce the reliance on human experts’ efforts and structural variation data extraction, it is necessary to establish a reliable and accurate chromothripsis detection method based on CNV data.

Results

To address those issues, we propose a method to detect chromothripsis solely based on CNV data. With the help of structure learning, the intrinsic relationship-directed acyclic graph of CNV features is inferred to derive a CNV embedding graph (i.e. CNV-DAG). Subsequently, a neural network based on Graph Transformer, local feature extraction, and non-linear feature interaction, is proposed with the embedding graph as the input to distinguish whether the chromothripsis event occurs. Ablation experiments, clustering, and feature importance analysis are also conducted to enable the proposed model to be explained by capturing mechanistic insights.

Availability and implementation

The source code and data are freely available at https://github.com/luvyfdawnYu/CNV_chromothripsis.
Floral morphology and development of Epimedium pubescens and Plagiorhegma dubium (Berberidaceae) and their systematic significance
Authors: Zhang Yu , Gong Jing-Zhi , Gui Chao-Xia , Xie Si-Yu , Yang Zhao-Ping , Luo Min-Rong , Ning Sui-Xia , Chang Zhao-Yang , Kang Bing , Zhao Liang , De Craene Louis Ronse
Published: June 2023

Epimedium and Plagiorhegma are the representatives of two early-diverging clades in Podophylloideae of Berberidaceae. Flowers are dimerous and trimerous respectively, but their floral development is little known. Here, we used scanning electron microscopy to clarify the structure and development of flower and inflorescence in Epimedium pubescens and Plagiorhegma dubium and compared these with other Berberidaceae to better understand floral evolution within the family. Our results show that the two genera share some significant features. The petal and stamen primordia emerge independently. The carpel is ascidiate from the earliest stages of development. The ovule is anatropous and bitegmic. However, E. pubescens has a paniculate inflorescence, while a lateral floral bud is initiated but aborted in P. dubium. In E. pubescens, both inner sepals and petals are delayed compared with other organs (vs. delay in petal development only for P. dubium). Petals of E. pubescens have a nectariferous spur (vs. no nectariferous tissue for P. dubium). The style of E. pubescens is long with a flat stigma (vs. short with trumpet-shaped stigma for P. dubium). These differences suggest that Epimedium and Plagiorhegma may not be closely related and support placing them separately in two clades of Podophylloideae, complementing results from molecular studies.
A mobile APP-based, customizable automated device for self-administered olfactory testing and an implementation of smell identification test
Authors: Lan Zhihao , Yang Qing X , Lyu Zhi-hong , Feng Cailing , Wang Liansheng , Ji Baowei , Yu Xuefei , Xin Sherman Xuegang
Published: June 2023

Olfactory tests are used for the evaluation of ability to detect and identify common odors in humans psychophysically. Olfactory tests are currently administered by professionals with a set of given odorants. Manual administration of such tests can be labor and cost intensive and data collected as such are confounded with experimental variables, which adds personnel costs and introduces potential errors and data variability. For large-scale and longitudinal studies, manually recorded data must be collected and compiled from multiple sites. It is difficult to standardize the way data are collected and recorded. There is a need for a computerized smell test system for psychophysical and clinical applications. A mobile digital olfactory testing system (DOTS) was developed, consisting of an odor delivery system (DOTS-ODD) and a mobile application program (DOTS-APP) connected wirelessly. The University of Pennsylvania Smell Identification Test was implemented in DOTS and compared to its commercial product on a cohort of 80 normosmic subjects and a clinical cohort of 12 Parkinson’s disease patients. A test–retest was conducted on 29 subjects of the normal cohort. The smell identification scores obtained from the DOTS and standard UPSIT commercial test are highly correlated (r = 0.714, P < 0.001), and test–retest reliability coefficient was 0.807 (r = 0.807, P < 0.001). The DOTS is customizable and mobile compatible, which allows for the implementation of standardized olfactory tests and the customization of investigators’ experimental paradigms. The DOTS-APP on mobile devices offers capabilities for a broad range of on-site, online, or remote clinical and scientific chemosensory applications.