Catchii: Empowering literature review screening in healthcare.

A systematic review is a type of literature review that aims to collect and analyse all available evidence from the literature on a particular topic. The process of screening and identifying eligible articles from the vast amounts of literature is a time-consuming task. Specialised software has been developed to aid in the screening process and save significant time and labour. However, the most suitable software tools that are available often come with a cost or only offer either a limited or a trial version for free. In this paper, we report the release of a new software application, Catchii, which contains all the important features of a systematic review screening application while being completely free. It supports a user at different stages of screening, from detecting duplicates to creating the final flowchart for a publication. Catchii is designed to provide a good user experience and streamline the screening process through its clean and user-friendly interface on both computers and mobile devices. All in all, Catchii is a valuable addition to the current selection of systematic review screening applications. It enables researchers without financial resources to access features found in the best paid tools, while also diminishing costs for those who have previously relied on paid applications. Catchii is available at https://catchii.org.

Benchmarking single-cell hashtag oligo demultiplexing methods.

Sample multiplexing is often used to reduce cost and limit batch effects in single-cell RNA sequencing (scRNA-seq) experiments. A commonly used multiplexing technique involves tagging cells prior to pooling with a hashtag oligo (HTO) that can be sequenced along with the cells' RNA to determine their sample of origin. Several tools have been developed to demultiplex HTO sequencing data and assign cells to samples. In this study, we critically assess the performance of seven HTO demultiplexing tools: hashedDrops, HTODemux, GMM-Demux, demuxmix, deMULTIplex, BFF (bimodal flexible fitting) and HashSolo. The comparison uses data sets where each sample has also been demultiplexed using genetic variants from the RNA, enabling comparison of HTO demultiplexing techniques against complementary data from the genetic 'ground truth'. We find that all methods perform similarly where HTO labelling is of high quality, but methods that assume a bimodal count distribution perform poorly on lower quality data. We also suggest heuristic approaches for assessing the quality of HTO counts in an scRNA-seq experiment.

Toblerone: detecting exon deletion events in cancer using RNA-seq.

Cancer is driven by mutations of the genome that can result in the activation of oncogenes or repression of tumour suppressor genes. In acute lymphoblastic leukemia (ALL) focal deletions in IKAROS family zinc finger 1 (IKZF1) result in the loss of zinc-finger DNA-binding domains and a dominant negative isoform that is associated with higher rates of relapse and  poorer patient outcomes. Clinically, the presence of IKZF1 deletions informs prognosis and treatment options. In this work we developed a method for detecting exon deletions in genes using RNA-seq with application to IKZF1. We developed a pipeline that first uses a custom transcriptome reference consisting of transcripts with exon deletions.  Next, RNA-seq reads are mapped using a pseudoalignment algorithm to identify reads that uniquely support deletions. These are then evaluated for evidence of the deletion with respect to gene expression and other samples. We applied the algorithm, named Toblerone, to a cohort of 99 B-ALL paediatric samples including validated IKZF1 deletions. Furthermore, we developed a graphical desktop app for non-bioinformatics users that can quickly and easily identify and report deletions in IKZF1 from RNA-seq data with informative graphical outputs.

Variants in SART3 cause a spliceosomopathy characterised by failure of testis development and neuronal defects.

Squamous cell carcinoma antigen recognized by T cells 3 (SART3) is an RNA-binding protein with numerous biological functions including recycling small nuclear RNAs to the spliceosome. Here, we identify recessive variants in SART3 in nine individuals presenting with intellectual disability, global developmental delay and a subset of brain anomalies, together with gonadal dysgenesis in 46,XY individuals. Knockdown of the Drosophila orthologue of SART3 reveals a conserved role in testicular and neuronal development. Human induced pluripotent stem cells carrying patient variants in SART3 show disruption to multiple signalling pathways, upregulation of spliceosome components and demonstrate aberrant gonadal and neuronal differentiation in vitro. Collectively, these findings suggest that bi-allelic SART3 variants underlie a spliceosomopathy which we tentatively propose be termed INDYGON syndrome (Intellectual disability, Neurodevelopmental defects and Developmental delay with 46,XY GONadal dysgenesis). Our findings will enable additional diagnoses and improved outcomes for individuals born with this condition.

propeller: testing for differences in cell type proportions in single cell data.

MOTIVATION: Single cell RNA-Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. This technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments, which has been difficult to directly address with bulk RNA-seq data, is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportion estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions. RESULTS: We have developed propeller, a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. Using simulated cell type proportions data, we show that propeller performs well under a variety of scenarios. We applied propeller to test for significant changes in cell type proportions related to human heart development, ageing and COVID-19 disease severity. AVAILABILITY AND IMPLEMENTATION: The propeller method is publicly available in the open source speckle R package (https://github.com/phipsonlab/speckle). All the analysis code for the article is available at the associated analysis website: https://phipsonlab.github.io/propeller-paper-analysis/. The speckle package, analysis scripts and datasets have been deposited at https://doi.org/10.5281/zenodo.7009042. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Large-scale manipulation of promoter DNA methylation reveals context-specific transcriptional responses and stability.

BACKGROUND: Cytosine DNA methylation is widely described as a transcriptional repressive mark with the capacity to silence promoters. Epigenome engineering techniques enable direct testing of the effect of induced DNA methylation on endogenous promoters; however, the downstream effects have not yet been comprehensively assessed. RESULTS: Here, we simultaneously induce methylation at thousands of promoters in human cells using an engineered zinc finger-DNMT3A fusion protein, enabling us to test the effect of forced DNA methylation upon transcription, chromatin accessibility, histone modifications, and DNA methylation persistence after the removal of the fusion protein. We find that transcriptional responses to DNA methylation are highly context-specific, including lack of repression, as well as cases of increased gene expression, which appears to be driven by the eviction of methyl-sensitive transcriptional repressors. Furthermore, we find that some regulatory networks can override DNA methylation and that promoter methylation can cause alternative promoter usage. DNA methylation deposited at promoter and distal regulatory regions is rapidly erased after removal of the zinc finger-DNMT3A fusion protein, in a process combining passive and TET-mediated demethylation. Finally, we demonstrate that induced DNA methylation can exist simultaneously on promoter nucleosomes that possess the active histone modification H3K4me3, or DNA bound by the initiated form of RNA polymerase II. CONCLUSIONS: These findings have important implications for epigenome engineering and demonstrate that the response of promoters to DNA methylation is more complex than previously appreciated.

RNA-Seq of amniotic fluid cell-free RNA: a discovery phase study of the pathophysiology of congenital cytomegalovirus infection.

BACKGROUND: Congenital cytomegalovirus infection is the most common perinatal infection and a significant cause of sensorineural hearing loss, cerebral palsy, and neurodevelopmental disability. There is a paucity of human gene expression studies examining the pathophysiology of cytomegalovirus infection. OBJECTIVE: This study aimed to perform a whole transcriptomic assessment of amniotic fluid from pregnancies with live fetuses to identify differentially expressed genes and enriched Gene Ontology categories associated with congenital cytomegalovirus infection. STUDY DESIGN: Amniotic fluid supernatant was prospectively collected from pregnant women undergoing amniocentesis for suspected congenital cytomegalovirus infection because of first-trimester maternal primary infection or ultrasound features suggestive of fetal infection. Women who had received therapy to prevent fetal infection were excluded. Congenital cytomegalovirus infection was diagnosed via viral polymerase chain reaction of amniotic fluid; cytomegalovirus-infected fetuses were paired with noninfected controls, matched for gestational age and fetal sex. Paired-end RNA sequencing was performed on amniotic fluid cell-free RNA with the Novaseq 6000 at a depth of 30 million reads per sample. Following quality control and filtering, reads were mapped to the human genome and counts summarized across genes. Differentially expressed genes were identified using 2 approaches: voomWithQualityWeights in conjunction with limma and RUVSeq with edgeR. Genes with a false discovery rate <0.05 were considered statistically significant. Differential exon use was analyzed using DEXSeq. Functional analysis was performed using gene set enrichment analysis and Ingenuity Pathway Analysis. Manual curation of differentially regulated genes was also performed. RESULTS: Amniotic fluid samples were collected from 50 women; 16 (32%) had congenital cytomegalovirus infection confirmed by polymerase chain reaction. After excluding 3 samples without matched controls, 13 cytomegalovirus-infected samples collected at 18 to 23 weeks and 13 cytomegalovirus-negative gestation-matched controls were submitted for RNA sequencing and analysis (N=26). Ten of the 13 pregnancies with cytomegalovirus-infected fetuses had amniocentesis because of serologic evidence of maternal primary infection with normal fetal ultrasound, and 3 had amniocentesis because of ultrasound abnormality suggestive of cytomegalovirus infection. Four cytomegalovirus-infected pregnancies ended in termination (n=3) or fetal death (n=1), and 9 resulted in live births. Pregnancy outcomes were available for 11 of the 13 cytomegalovirus-negative controls; all resulted in live births of clinically-well infants. Differential gene expression analysis revealed 309 up-regulated and 32 down-regulated genes in the cytomegalovirus-infected group compared with the cytomegalovirus-negative group. Gene set enrichment analysis showed significant enrichment of multiple Gene Ontology categories involving the innate immune response to viral infection and interferon signaling. Of the 32 significantly down-regulated genes, 8 were known to be involved in neurodevelopment and preferentially expressed by the brain. Six specific cellular restriction factors involved in host defense to cytomegalovirus infection were up-regulated in the cytomegalovirus-infected group. Ingenuity Pathway Analysis predicted the activation of pathways involved in progressive neurologic disease and inflammatory neurologic disease. CONCLUSION: In this next-generation sequencing study, we revealed new insights into the pathophysiology of congenital cytomegalovirus infection. These data on the up-regulation of the intraamniotic innate immune response to cytomegalovirus infection and the dysregulation of neurodevelopmental genes may inform future approaches to developing prognostic markers and assessing fetal responses to in utero therapy.

STRipy: A graphical application for enhanced genotyping of pathogenic short tandem repeats in sequencing data.

Expansions of short tandem repeats (STRs) have been implicated as the causal variant in over 50 diseases known to date. There are several tools which can genotype STRs from high-throughput sequencing (HTS) data. However, running these tools out of the box only allows around half of the known disease-causing loci to be genotyped. Furthermore, the genotypes estimated at these loci are often underestimated with maximum lengths limited to either the read or fragment length, which is less than the pathogenic cutoff for some diseases. Although analysis tools can be customized to genotype extra loci, this requires proficiency in bioinformatics to set up, limiting their widespread usage by other researchers and clinicians. To address these issues, we have developed a new software called STRipy, which is able to target all known disease-causing STRs from HTS data. We created an intuitive graphical interface for STRipy and significantly simplified the detection of STRs expansions. Moreover, we genotyped all disease loci for over two and half thousand samples to provide population-wide distributions to assist with interpretation of results. We believe the simplicity and breadth of STRipy will increase the genotyping of STRs in sequencing data resulting in further diagnoses of rare STR diseases.

Enhancer retargeting of CDX2 and UBTF::ATXN7L3 define a subtype of high-risk B-progenitor acute lymphoblastic leukemia.

Transcriptome sequencing has identified multiple subtypes of B-progenitor acute lymphoblastic leukemia (B-ALL) of prognostic significance, but a minority of cases lack a known genetic driver. Here, we used integrated whole-genome (WGS) and -transcriptome sequencing (RNA-seq), enhancer mapping, and chromatin topology analysis to identify previously unrecognized genomic drivers in B-ALL. Newly diagnosed (n = 3221) and relapsed (n = 177) B-ALL cases with tumor RNA-seq were studied. WGS was performed to detect mutations, structural variants, and copy number alterations. Integrated analysis of histone 3 lysine 27 acetylation and chromatin looping was performed using HiChIP. We identified a subset of 17 newly diagnosed and 5 relapsed B-ALL cases with a distinct gene expression profile and 2 universal and unique genomic alterations resulting from aberrant recombination-activating gene activation: a focal deletion downstream of PAN3 at 13q12.2 resulting in CDX2 deregulation by the PAN3 enhancer and a focal deletion of exons 18-21 of UBTF at 17q21.31 resulting in a chimeric fusion, UBTF::ATXN7L3. A subset of cases also had rearrangement and increased expression of the PAX5 gene, which is otherwise uncommon in B-ALL. Patients were more commonly female and young adult with median age 35 (range,12-70 years). The immunophenotype was characterized by CD10 negativity and immunoglobulin M positivity. Among 16 patients with known clinical response, 9 (56.3%) had high-risk features including relapse (n = 4) or minimal residual disease >1% at the end of remission induction (n = 5). CDX2-deregulated, UBTF::ATXN7L3 rearranged (CDX2/UBTF) B-ALL is a high-risk subtype of leukemia in young adults for which novel therapeutic approaches are required.

SFPQ-ABL1 and BCR-ABL1 use different signaling networks to drive B-cell acute lymphoblastic leukemia.

Philadelphia-like (Ph-like) acute lymphoblastic leukemia (ALL) is a high-risk subtype of B-cell ALL characterized by a gene expression profile resembling Philadelphia chromosome-positive ALL (Ph+ ALL) in the absence of BCR-ABL1. Tyrosine kinase-activating fusions, some involving ABL1, are recurrent drivers of Ph-like ALL and are targetable with tyrosine kinase inhibitors (TKIs). We identified a rare instance of SFPQ-ABL1 in a child with Ph-like ALL. SFPQ-ABL1 expressed in cytokine-dependent cell lines was sufficient to transform cells and these cells were sensitive to ABL1-targeting TKIs. In contrast to BCR-ABL1, SFPQ-ABL1 localized to the nuclear compartment and was a weaker driver of cellular proliferation. Phosphoproteomics analysis showed upregulation of cell cycle, DNA replication, and spliceosome pathways, and downregulation of signal transduction pathways, including ErbB, NF-κB, vascular endothelial growth factor (VEGF), and MAPK signaling in SFPQ-ABL1-expressing cells compared with BCR-ABL1-expressing cells. SFPQ-ABL1 expression did not activate phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) signaling and was associated with phosphorylation of G2/M cell cycle proteins. SFPQ-ABL1 was sensitive to navitoclax and S-63845 and promotes cell survival by maintaining expression of Mcl-1 and Bcl-xL. SFPQ-ABL1 has functionally distinct mechanisms by which it drives ALL, including subcellular localization, proliferative capacity, and activation of cellular pathways. These findings highlight the role that fusion partners have in mediating the function of ABL1 fusions.

JAFFAL: detecting fusion genes with long-read transcriptome sequencing.

In cancer, fusions are important diagnostic markers and targets for therapy. Long-read transcriptome sequencing allows the discovery of fusions with their full-length isoform structure. However, due to higher sequencing error rates, fusion finding algorithms designed for short reads do not work. Here we present JAFFAL, to identify fusions from long-read transcriptome sequencing. We validate JAFFAL using simulations, cell lines, and patient data from Nanopore and PacBio. We apply JAFFAL to single-cell data and find fusions spanning three genes demonstrating transcripts detected from complex rearrangements. JAFFAL is available at https://github.com/Oshlack/JAFFA/wiki .

splatPop: simulating population scale single-cell RNA sequencing data.

Population-scale single-cell RNA sequencing (scRNA-seq) is now viable, enabling finer resolution functional genomics studies and leading to a rush to adapt bulk methods and develop new single-cell-specific methods to perform these studies. Simulations are useful for developing, testing, and benchmarking methods but current scRNA-seq simulation frameworks do not simulate population-scale data with genetic effects. Here, we present splatPop, a model for flexible, reproducible, and well-documented simulation of population-scale scRNA-seq data with known expression quantitative trait loci. splatPop can also simulate complex batch, cell group, and conditional effects between individuals from different cohorts as well as genetically-driven co-expression.

MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data.

Calling fusion genes from RNA-seq data is well established, but other transcriptional variants are difficult to detect using existing approaches. To identify all types of variants in transcriptomes we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference-free approach, combining de novo assembly of transcripts with differential expression analysis to identify up-regulated novel variants in a case sample. We compare MINTIE with eight other approaches, detecting > 85% of variants while no other method is able to achieve this. We posit that MINTIE will be able to identify new disease variants across a range of disease types.

Detecting copy number alterations in RNA-Seq using SuperFreq.

MOTIVATION: Calling copy number alterations (CNAs) from RNA sequencing (RNA-Seq) is challenging, because of the marked variability in coverage across genes and paucity of single nucleotide polymorphisms (SNPs). We have adapted SuperFreq to call absolute and allele sensitive CNAs from RNA-Seq. SuperFreq uses an error-propagation framework to combine and maximize information from read counts and B-allele frequencies. RESULTS: We used datasets from The Cancer Genome Atlas (TCGA) to assess the validity of CNA calls from RNA-Seq. When ploidy estimates were consistent, we found agreement with DNA SNP-arrays for over 98% of the genome for acute myeloid leukaemia (TCGA-AML, n = 116) and 87% for colorectal cancer (TCGA-CRC, n = 377). The sensitivity of CNA calling from RNA-Seq was dependent on gene density. Using RNA-Seq, SuperFreq detected 78% of CNA calls covering 100 or more genes with a precision of 94%. Recall dropped for focal events, but this also depended on signal intensity. For example, in the CRC cohort SuperFreq identified all cases (7/7) with high-level amplification of ERBB2, where the copy number was typically >20, but identified only 6% of cases (1/17) with moderate amplification of IGF2, which occurs over a smaller interval. SuperFreq offers an integrated platform for identification of CNAs and point mutations. As evidence of how SuperFreq can be applied, we used it to reproduce the established relationship between somatic mutation load and CNA profile in CRC using RNA-Seq alone. AVAILABILITY AND IMPLEMENTATION: SuperFreq is implemented in R and the code is available through GitHub: https://github.com/ChristofferFlensburg/SuperFreq/. Data and code to reproduce the figures are available at: https://gitlab.wehi.edu.au/flensburg.c/SuperFreq_RNA_paper. Data from TCGA (phs000178) was accessed from GDC following completion of a data access request through the database of Genotypes and Phenotypes (dbGaP). Data from the Leucegene consortium was downloaded from GEO (AML samples: GSE67040; normal CD34+ cells: GSE48846). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene set enrichment analysis for genome-wide DNA methylation data.

DNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.