Biology subjects

Hleap, J. S.

Publications and source records attributed to Hleap, J. S..

9 recordsLinked to original sources

bioRxiv2026

SCALLOPS: a scalable, integrated computational framework for Optical Pooled Screens

Optical pooled screens (OPS) link pooled genetic perturbations to high-dimensional image-based phenotypes at scale, but their widespread adoption is hindered by computational bottlenecks in processing terabyte-scale, multimodal image data. We present SCALLOPS, a unified, modular, and cloud-native computational framework that overcomes these bottlenecks. SCALLOPS implements a "well-centric" processing strategy that integrates robust stitching with a non-linear two-stage registration strategy, enabling accurate alignment of multi-magnification images, reliable single-cell genotype-phenotype linkage, and efficient morphological feature extraction. Benchmarking with public and newly-generated datasets demonstrated SCALLOPS superior performance over existing solutions. Crucially, SCALLOPS uniquely enables robust processing of 4x magnification in situ sequencing data, accelerating image acquisition by around six-fold. We applied SCALLOPS to an optical pooled screen investigating the estrogen receptor (ER) degrader vepdegestrant in a breast cancer cell line, successfully recovering its known mechanism of action, highlighting the value of OPS in translational research. SCALLOPS provides a scalable end-to-end solution, making large-scale OPS routine.

bioinformatics↗

bioRxiv2023

Highly multiplexed, image-based pooled screens in primary cells and tissues with PerturbView

Optical pooled screening (OPS) is a highly scalable method for linking image-based phenotypes with cellular perturbations. However, it has thus far been restricted to relatively low-plex phenotypic readouts in cancer cell lines in culture, due to limitations associated with in situ sequencing (ISS) of perturbation barcodes. Here, we developed PerturbView, an OPS technology that leverages in vitro transcription (IVT) to amplify barcodes prior to ISS, enabling screens with highly multiplexed phenotypic readouts across diverse systems, including primary cells and tissues. We demonstrate PerturbView in iPSC-derived neurons, primary immune cells, and tumor tissue sections from animal models. In a screen of immune signaling pathways in primary bone marrow-derived macrophages, PerturbView uncovered both known and novel regulators of NF{kappa}B signaling. Furthermore, we combined PerturbView with spatial transcriptomics in tissue sections from a mouse xenograft model, paving the way to in vivo screens with rich optical and transcriptomic phenotypes. PerturbView broadens the scope of OPS to a wide range of models and applications.

systems biology↗

bioRxiv2022

Freshwater connectivity transforms spatially integrated signals of biodiversity

Aquatic ecosystems offer a continuum of water flow from headwater streams to inland lakes and coastal marine systems. This spatial connectivity influences the structure, function and dynamics of aquatic communities, which are among the most threatened and degraded on earth. Environmental DNA achieves biodiversity surveys in these habitats in a high-throughput, spatially integrated way. Here, we determine the spatial resolution of eDNA in dendritic freshwater networks that are typical of aquatic habitats. Our intensive sampling campaign comprised over 430 eDNA samples across 21 connected lakes, allowing us to analyse detections at a variety of scales, from different habitats within a lake to entire lake networks. We found strong signals of within-lake variation in eDNA distribution reflective of typical habitat use by both fish and zooplankton. Most importantly, we also found that connecting channels between lakes resulted in an accumulation of downstream eDNA detections in lakes with a higher number of inflows, and as networks increased in length. These findings have profound implications for the interpretation of eDNA detections in aquatic ecosystems in global-scale biodiversity monitoring observations.

ecology↗

bioRxiv2022

Metagenomics vs. total RNA sequencing: most accurate data-processing tools, microbial identification accuracy, and implications for freshwater assessments

Metagenomics and total RNA sequencing (total RNA-Seq) have the potential to improve the taxonomic identification of diverse microbial communities, which could allow for the incorporation of microbes into routine freshwater assessments. However, these targeted-PCR- free techniques require more testing and optimization. In this study, we processed metagenomics and total RNA-Seq data from a commercially available microbial mock community using 768 data-processing workflows, identified the most accurate data-processing tools, and compared their microbial identification accuracy at equal and increasing sequencing depths. The accuracy of data-processing tools substantially varied among replicates. Total RNA-Seq was more accurate than metagenomics at equal sequencing depths and even at sequencing depths almost one order of magnitude lower than those of metagenomics. We show that while data-processing tools require further exploration, total RNA-Seq might be a favorable alternative to metagenomics for targeted-PCR-free taxonomic identifications of microbial communities and might enable a substantial reduction in sequencing costs while maintaining accuracy. This could be particularly an advantage for routine freshwater assessments, which require cost-effective yet accurate methods, and might allow for the incorporation of microbes into freshwater assessments.

genomics↗

bioRxiv2020

Assessment of current taxonomic assignment strategies for metabarcoding eukaryotes

The effective use of metabarcoding in biodiversity science has brought important analytical challenges due to the need to generate accurate taxonomic assignments. The assignment of sequences to a generic or species level is critical for biodiversity surveys and biomonitoring, but it is particularly challenging. Researchers must select the approach that best recovers information on species composition. This study evaluates the performance and accuracy of seven methods in recovering the species composition of mock communities which vary in species number and specimen abundance, while holding upstream molecular and bioinformatic variables constant. It also evaluates the impact of parameter optimization on the quality of the predictions. Despite the general belief that BLAST top hit underperforms newer methods, our results indicate that it competes well with more complex approaches if optimized for the mock community under study. For example, the two machine learning methods that were benchmarked proved more sensitive to the reference database heterogeneity and completeness than methods based on sequence similarity. The accuracy of assignments was impacted by both species and specimen counts which will influence the selection of appropriate software. We urge the usage of realistic mock communities to allow optimization of parameters, regardless of the taxonomic assignment method used.

bioinformatics↗

bioRxiv2020

A2G2: A Python wrapper to perform very largealignments in semi-conserved regions

SummaryAmplicons to Global Gene (A2G2) is a Python wrapper that uses MAFFT and an "Amplicon to Gene" strategy to align very large numbers of sequences while improving alignment accuracy. It is specially developed to deal with conserved genes, where traditional aligners introduce a significant amount of gaps. A2G2 leverages the add sequences option of MAFFT to align the sequences to a global reference gene and a local reference region. Both of these references can be consensus sequences of trusted sources. Efficient parallelization of these tasks allows A2G2 to align a very large number of sequences (> 500K) in a reasonable amount of time. A2G2 can be imported in Python for easier integration with other software, or can be run via command line. AvailabilityA2G2 is implemented in Python 3 (3.6) and depends on MAFFT availability. Other package requirements can be found in the requirements.txt file at https://github.com/jshleap/A2G. A2G2 is also available via PyPi (https://pypi.org/project/A2G). It is licensed under the LGPLv3. Supplementary informationSupplementary material is available at github as jupyter notebook.

bioinformatics↗

bioRxiv2017

Protein structures as shapes: Analysing protein structure variation using geometric morphometrics

A phenotype is defined as an organisms physical traits. In the macroscopic world, an animals shape is a phenotype. Geometric morphometrics (GM) can be used to analyze its shape. Lets pose protein structures as microscopic three dimensional shapes, and apply principles of GM to the analysis of macromolecules. In this paper we introduce a way to 1) abstract a structure as a shape; 2) align the shapes; and 3) perform statistical analysis to establish patterns of variation in the datasets. We show that general procrustes superimposition (GPS) can be replaced by multiple structure alignment without changing the outcome of the test. We also show that estimating the deformation of the shape (structure) can be informative to analyze relative residue variations. Finally, we show an application of GM for two protein structure datasets: 1) in the -amylase dataset we demonstrate the relationship between structure, function, and how the dependency of chloride has an important effect on the structure; and 2) in the Niemann-Pick disease, type C1 (NPC1) proteins molecular dynamic simulation dataset, we introduce a simple way to analyze the trajectory of the simulation by means of protein structure variation.

bioinformatics↗

bioRxiv2017

The response to selection in Glycoside Hydrolase Family 13 structures: A comparative quantitative genetics approach

The Glycoside Hydrolase Family 13 (GH13) is both evolutionary diverse and relevant to many industrial applications. Its members perform the hydrolysis of starch into smaller carbohydrates. Members of the family have been bioengineered to improve catalytic function under industrial environments. We introduce a framework to analyze the response to selection of GH13 protein structures given some phylogenetic and simulated dynamic information. We found that the TIM-barrel is not selectable since it is under purifying selection. We also show a method to rank important residues with higher inferred response to selection. These residues can be altered to effect change in properties. In this work, we define fitness as inferred thermodynamic stability. We show that under the developed framework, residues 112Y, 122K, 124D, 125W, and 126P are good candidates to increase the stability of the truncated protein 4E2O. Overall, this paper demonstrate the feasibility of a framework for the analysis of protein structures for any other fitness landscape.

bioinformatics↗

bioRxiv2017

The response to selection in Glycoside Hydrolase Family 13 structures: A comparative quantitative genetics approach

Glycoside Hydrolase Family 13 (GH13) structures are responsible for the hydrolysis of starch into smaller carbohydrates. They important in industrial applications and evolutionary studies. This family has been thoroughly documented in the the Carbohydrate-Active enZYmes Database (CAZY), and divided into subfamilies based mainly in sequence information. Here we give structural evidence into GH13 classification and evolution using structural information. Here we proposed a novel method that is sensitive enough to identify miss-classifications, or to provide evidence for further partition that can be of interests to bio-engineers and evolutionary biologists. We also introduced a method to explore the relative importance of residues with respect to the overall deformation that it causes to the overall structure in an evolutionary time scale. We found that the GH13 family can be classified into three main structural groups. There is a hierarchical structure within these clusters that can be use to inform other classification schemes. We also found that by using structural information, subtle structural shifts can be identified and that can be missed in sequence/phylogeny-only based classifications. When each structural group is explored, we found that identifying the most structurally variable sites can lead to identification of functionally (both catalytically and structurally) important residues.

bioinformatics↗