Biology subjects

Sarfraz, I.

Publications and source records attributed to Sarfraz, I..

6 recordsLinked to original sources

bioRxiv2025

High-Quality PacBio Genome Assembly of Populus alba L. Villafranca

This study presents the high-quality genome assemblies for Populus alba L. "Villafranca" using PacBio HiFi sequencing. The assembly span 498.95 Mb, an N50 of 18.18 Mb and largest contig of 52.03 Mb. BUSCO analysis revealed genome completeness (embryophyta_odb10) with 98.8% of the 1,614 BUSCO groups searched. The Transposable element and repetitive content accounted for [~]31.37%. The comparison of P. alba and P. trichocarpa genomes identified 9,741 structural variants (SVs) This comprehensive analysis provides valuable resources for studying poplar genome evolution, domestication, and genetic improvement, underscoring the utility of long-read sequencing for resolving complex genomic features.

genomics↗

bioRxiv2024

PacBio genome assembly of Olea europaea L. subsp. europaea cultivars 'Frantoio' and 'Leccino' reveal main structural differences in key genes related to salt stress

We present two high-quality genome assemblies for Olea europaea L. cultivars Frantoio and Leccino leveraging PacBio HiFi sequencing to achieve approximately 30x genome coverage for each cultivar. The assemblies span 1.18 Gbp and 1.43 Gbp with contig N50 values of 1.78 Mbp and 45.88 Mbp for Frantoio and Leccino respectively. BUSCO analysis revealed a great genome completeness ([~]97.9%), surpassing many of earlier Olea europaea assemblies and is in par with the most recent one. Repetitive content accounted for [~]67.5% in Frantoio and [~]70.8% in Leccino with long terminal repeats (LTRs) dominating. Notably, a tandem repeat family, Satellite 1, represented [~]16.9% and [~]8.6% of the Leccino and Frantoio genomes, respectively. The structural variant (SV) analysis was done with a particular focus on those associated with nine key gene families involved in salinity tolerance and identified cultivar-specific genomic differences, emphasizing the diversity within domesticated olives. This comprehensive analysis provides valuable resources for studying olive genome evolution, domestication, and genetic improvement, underscoring the utility of long-read sequencing for resolving complex genomic features.

genomics↗

bioRxiv2023

Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis ecosystem

Emerging spatial omics technologies continue to advance the molecular mapping of tissue architecture and the investigation of gene regulation and cellular crosstalk, which in turn provide new mechanistic insights into a wide range of biological processes and diseases. Such technologies provide an increasingly large amount of information content at multiple spatial scales. However, representing and harmonizing diverse spatial datasets efficiently, including combining multiple modalities or spatial scales in a scalable and flexible manner, remains a substantial challenge. Here, we present Giotto Suite, a suite of open-source software packages that underlies a fully modular and integrated spatial data analysis toolbox. At its core, Giotto Suite is centered around an innovative and technology-agnostic data framework embedded in the R software environment, which allows the representation and integration of virtually any type of spatial omics data at any spatial resolution. In addition, Giotto Suite provides both scalable and extensible end-to-end solutions for data analysis, integration, and visualization. Giotto Suite integrates molecular, morphology, spatial, and annotated feature information to create a responsive and flexible workflow for multi-scale, multi-omic data analyses, as demonstrated here by applications to several state-of-the-art spatial technologies. Furthermore, Giotto Suite builds upon interoperable interfaces and data structures that bridge the established fields of genomics and spatial data science, thereby enabling independent developers to create custom-engineered pipelines. As such, Giotto Suite creates an immersive ecosystem for spatial multi-omic data analysis.

bioinformatics↗

bioRxiv2023

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data

A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex "use cases" to characterize the types of featureobservation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple listlike schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/.

bioinformatics↗

bioRxiv2022

Interactive analysis of single-cell data using flexible workflows with SCTK2.0

Analysis of single-cell RNA-seq (scRNA-seq) data can reveal novel insights into heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analysis. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single Cell Toolkit 2.0 (SCTK2.0), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/499900v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@2a3964org.highwire.dtl.DTLVardef@1debe9org.highwire.dtl.DTLVardef@6b3a2aorg.highwire.dtl.DTLVardef@1b7bf84_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIIntuitive graphical user interface for interactive analysis of scRNA-seq data C_LIO_LIAllows non-computational users to analyze scRNA-seq data with end-to-end workflows C_LIO_LIProvides interoperability between tools across different programming environments C_LIO_LIProduces HTML reports for reproducibility and easy sharing of results C_LI

bioinformatics↗

bioRxiv2020

Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, a variety of technical artifacts can be present in scRNA-seq data and need to be assessed before downstream analyses can be performed. While several algorithms and tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Comprehensive pipelines to streamline the process of generating and visualizing QC metrics are lacking. To address this need, we built the SCTK-QC pipeline within the singleCellTK R package (https://github.com/compbiomed/singleCellTK). Features in this pipeline include the ability to import data from 11 different preprocessing tools or file formats, perform empty droplet detection with 2 different algorithms, generate standard quality control metrics such as number of UMIs per cell or the percentage of mitochondrial counts, predict doublets using 6 different algorithms, and estimate ambient RNA. QC data can be exported to R and/or Python objects used in popular down-stream workflows. Results are visualized in an easy-to-read HTML report. This pipeline can also be used by non-computational users with an interactive graphical user interface developed with R/Shiny. Overall, the SCTK-QC pipeline will streamline and standardize QC analysis for scRNA-seq data across a variety of different single-cell transcriptomic platforms and preprocessing tools.

bioinformatics↗