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Open Source Biology & Genetics Interest Group

Open source scripts, reports, and preprints for in vitro biology, genetics, bioinformatics, crispr, and other biotech applications.

 Posted in Research

Can there be a lot of misalignment with a 150bp paired-ended reading frame?

 October 9, 2021

Can there be a lot of misalignment with a 150bp paired-ended reading frame?

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For example, is it possible to have sequences displayed on IGV at a position in the genome that is wrong? Is this common?


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CRISPR Web Analyser Göknur Giner (WEHI, Australia) 4:30 PM - 4:45 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 LONG TALK CRISPR/Cas-based genome editing systems have revolutionised the field of genome engineering with applications in a plethora of research fields, including medicine, biology, and biotechnology in only a few years. Here we present a newly developed CRISPR web analyser that offers a few modules to understand the count data obtained from next-generation sequencing (NGS) technology of a pooled CRISPR experiment. These modules include Data Quality Check, Data Preprocessing, and Fitting a Statistical Model. Moderator: Peter Hickey (WEHI, Australia)
CRISPR Web Analyser
Removing Unwanted Variation Using Pseudoreplicates and Pseudosamples Ramyar Molania (WEHI, Australia) 4:15 PM - 4:30 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 LONG TALK Accurate identification and effective removal of unwanted variation is essential to derive meaningful biological results from RNA sequencing (RNA-seq) data, especially when the data come from large and complex studies. Using RNA-seq data from The Cancer Genome Atlas (TCGA), we examined several sources of unwanted variation and demonstrate here how these can signifi- cantly compromise various downstream analyses, including cancer subtype identification, association between gene expression and survival outcomes and gene co-expression analysis. We propose a strategy, called pseudo-replicates of pseudo-samples (PRPS), for deploying our recently developed normalization method, called removing unwanted variation III (RUV-III), to remove the variation caused by library size, tumor purity and batch effects in TCGA RNA-seq data. We illustrate the value of our approach by comparing it to the standard TCGA normalizations on several TCGA RNA-seq datasets. RUV-III with PRPS can be used to integrate and normalize other large transcriptomic datasets coming from multiple laboratories or platforms. Moderator: Peter Hickey (WEHI, Australia)
Removing Unwanted Variation Using Pseudoreplicates and Pseudosamples
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Identifying Differentially Abundant Phosphoproteome Sites With ProteomeRiver
Notes: The audio for the first 6 minutes of this talk was not recorded due to an error with the hybrid meeting. Extension of scPipe Bioconductor Package for scATAC-seq Data Shanika Amarasinghe (ARMI/WEHI, Australia) 3:30 PM - 3:45 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 LONG TALK Single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enables genome-wide profiling of chromatin accessibility that can be used to improve gene expression modelling at single-cell resolution. scATAC-seq data are intrinsically sparse, since the signals detected at a genomic position are limited by DNA copy number, thereby making it difficult to discriminate signals from noise. Therefore, thorough and clear pre-processing steps are needed prior to downstream analyses. Furthermore, these pre-processing steps need to be sufficiently adaptable to user requirements. Currently available scATAC-seq pre-processing tools are either not fully R-based or have limited flexibility. We have extended scPipe, our current pre-processing tool for single-cell RNA data, to be able to process scATAC-seq data from a variety of different protocols. We take the power and flexibility of the R environment and create a workflow that combines multiple Bioconductor packages to achieve this. We also evaluated the performance of scPipe’s scATAC-Seq module on an in-house benchmarking dataset to assess its speed and ability to recover known population structure. Moderator: Peter Hickey (WEHI, Australia)
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Matilda for Single-cell Multi-omics Data Integration Chunlei Liu (Childrens Medical Research Institute, Australia) 2:55 PM - 3:00 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 PACKAGE DEMO Single-cell multimodal omics technologies enable multiple molecular programs to be simultaneously profiled at a global scale in individual cells, creating opportunities to study biological systems at a resolution that was previously inaccessible. However, the analysis of single-cell multimodal omics data is challenging due to the lack of methods that can integrate across multiple data modalities generated from such technologies. Here, we present Matilda, a multi-task learning method for integrative analysis of single-cell multimodal omics data. By leveraging the interrelationship among tasks, Matilda learns to perform data simulation, dimension reduction, cell type classification, and feature selection in a single unified framework. We compare Matilda with other state-of-the-art methods on datasets generated from some of the most popular single-cell multimodal omics technologies. Our results demonstrate the utility of Matilda for addressing multiple key tasks on integrative single-cell multimodal omics data analysis. Moderator: Peter Hickey (WEHI, Australia) and Anna Quaglieri (Mass Dynamics, Australia)
Matilda for Single-cell Multi-omics Data Integration
cellXY for Exploring Gender-specific Genes in Single Cell RNA-seq Data Melody Jin (WEHI, Australia) 2:15 PM - 2:30 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 LONG TALK Single cell RNA-seq is a revolutionary technology that has enabled the discovery of novel cell types and has facilitated deeper insight into normal tissue development. Single cell data is highly noisy, and extensive research focuses on quality control strategies. We develop a package cellXY (https://github.com/phipsonlab/cellXY) with pre-trained machine learning models to qualify the data from the cell sex perspective. We define super genes (superX and superY) to capture the expression pattern of male and female cells. Several models are tuned to predict the sex label for human and mouse cells, including logistic regression, support vector machines, random forest and gradient boosting machine models. The performance on a pooled human PBMC dataset with available ground truth reaches an overall sensitivity of 0.9016 and a precision of 0.9053. Besides, we test the developed model on a public mouse heart dataset without true labels, and successfully predict 37% more cells compared to a simple thresholding method originally adopted by the researchers. Additionally, we rely on sex-related features to train models identifying Male/Female doublets with specially generated doublet-singlet training sets. We achieve optimal performances in the training phase, and the identified Male/Female doublets in public datasets exhibit clear evidence based on feature distributions. The package is publically available and can contribute to the quality control process in single-cell data analysis. It can be used in conjunction with the demultiplexing techniques to assist the cell-to-sample allocation. Moderator: Peter Hickey (WEHI, Australia) and Anna Quaglieri (Mass Dynamics, Australia)
cellXY for Exploring Gender-specific Genes in Single Cell RNA-seq Data
Stereopy as an Advanced Tool for Interpreting Spatial Transcriptomics Data Taylor Tian (BGI, Australia) 2:45 PM - 2:55 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 PACKAGE DEMO Moderator: Anna Quaglieri (Mass Dynamics, Australia)
Stereopy as an Advanced Tool for Interpreting Spatial Transcriptomics Data
Spectre Toolkit for Rapid Analysis of Cytometry Data Givanna Putri (WEHI, Australia) 2:00 PM - 2:15 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 LONG TALK Cytometry is a technology which simultaneously, rapidly, and economically quantifies the expression of up to 50 proteins of millions of cells at single-cell resolution. It is routinely used in studies to investigate immune system, infectious diseases, and cancer. While cytometry and scRNAseq share few common data analysis steps, computational approaches specific for cytometry data are not as well established as those for scRNAseq data, and that it is non-trivial for scRNAseq tools to process cytometry data due to the sheer volume of the data on hand (millions vs thousands of cells) requiring excessive processing time and computing resources. Importantly, it is often difficult to integrate existing computational tools in an analysis workflow, as they frequently operate on custom complex data formats. Existing toolkits are inflexible in that they also use custom overly complicated data structures and often mandate a strict order of analysis operations, some of which are not required for cytometry data. To address this, we developed Spectre, a flexible R toolkit for rapid analysis of cytometry data. Spectre facilitates the creation of a highly modular analysis workflow by employing data.table (an extension of R’s native data.frame format) for its data management and operations, and by implementing wrapper functions for select essential computational tools to perform data pre-processing, batch alignment, clustering, dimensionality reduction, plotting, and simple statistical analyses, such that they can be easily chained together in any given order in a workflow. Further, Spectre also facilitates cell type annotation label transfer across datasets using classical machine learning classification algorithms. Since its inception, Spectre has been used in various biological studies, including one which studied the immune response to COVID-19 infection. Here, we illustrate the use of Spectre by analysing cytometry data elucidating the immune response to the West Nile virus infection in mice. In summary, Spectre facilitates the analysis of complex high-dimensional cytometry data by enabling seamless integration of computational methods through strategic use of data.table and wrapper functions. Further work is currently underway to develop the infrastructure and wrapper functions to analyse Imaging Mass Cytometry data and to integrate cytometry with CITEseq or AbSeq data. Spectre is freely available on GitHub https://github.com/immunedynamics/spectre. Moderator: Anna Quaglieri (Mass Dynamics, Australia)
Spectre Toolkit for Rapid Analysis of Cytometry Data
A Bioconductor Framework for High-dimensional in situ Cytometry Analysis Ellis Patrick (The University of Sydney, Australia) 1:45 PM - 2:00 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 LONG TALK Understanding the interplay between different types of cells and their immediate environment is critical for understanding the mechanisms of cells themselves and their function in the context of human diseases. Recent advances in high dimensional in situ cytometry technologies have fundamentally revolutionized our ability to observe these complex cellular relationships providing an unprecedented characterisation of cellular heterogeneity in a tissue environment. We have developed an analytical framework for analysing data from high dimensional in situ cytometry assays including CODEX, CycIF, IMC and High Definition Spatial Transcriptomics. Implemented in R, this framework makes use of functionality from our Bioconductor packages spicyR, lisaClust, scFeatures, treekoR, FuseSOM, simpleSeg and ClassifyR. We will provide an overview of key steps which are needed to interrogate the comprehensive spatial information generated by these exciting new technologies including cell segmentation, feature normalisation, cell type identification, microenvironment characterisation, spatial hypothesis testing and patient classification. Ultimately, our modular analysis framework provides a cohesive and accessible entry point into spatially resolved single cell data analysis for any R-based bioinformaticians. Moderator: Anna Quaglieri (Mass Dynamics, Australia)
A Bioconductor Framework for High-dimensional in situ Cytometry Analysis
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