Alicia Oshlack

The paper introduces clustering trees to visualize clustering results at multiple resolutions and demonstrates their use on simulated and real datasets.

Clinker visualizes fusion genes detected in RNA-seq data, enabling interpretable analysis of fusion transcripts and breakpoints in B-ALL.

The paper introduces the scRNA-tools database to catalog and curate scRNA-seq analysis tools, highlighting trends in tool development and open-source practices.

Necklace combines reference and assembled transcriptomes to create a comprehensive superTranscriptome for RNA-Seq analysis, enabling more accurate differential expression testing.

Splatter simulates single-cell RNA-seq data using a gamma-Poisson model, offering reproducible and well-documented simulations for multiple cell types and differentiation paths.

This paper investigates gene length bias in scRNA-seq across different protocols and datasets, finding that UMI-based protocols reduce gene length bias compared to full-length transcript protocols.

SuperTranscript provides a reference alternative to genomes for transcriptome analysis in non-model organisms, enabling visualization and variant detection using existing tools.

The paper provides a Bioconductor workflow for analysing methylation array data, covering quality control, normalization, differential methylation analysis, and visualization.

Cpipe is a variant detection pipeline designed for clinical genomics, offering fast, reproducible, and customizable analysis for diagnostic settings.

The paper presents a 2-stage approach using RUV-inverse to remove unwanted variation in differential methylation analysis of Illumina HumanMethylation450 array data, showing improved performance over existing methods.

JAFFA detects fusion genes in cancer transcriptomes using long reads or de novo assembled short reads, outperforming existing methods with 100bp or longer reads.

This study compares WCE and H3 ChIP-seq as control samples for histone modification ChIP-seq, finding minor differences but negligible impact on analysis quality.

DiffVar detects differential variability in methylation data across sample groups, applied to cancer and aging datasets.