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T-cell acute lymphoblastic leukaemia (T-ALL) is an aggressive and heterogenous haematological malignancy affecting both children and adults. T-ALL subtype identification is an emerging area of active research, with several recent studies proposing potential subtypes based on transcriptomic and genomic analyses. Here we present TALLSorts, a machine-learning bioinformatic tool which classifies T-ALL samples by using bulk RNA sequencing (RNA-seq) data. Trained on four international cohorts totalling 264 samples, TALLSorts exhibits excellent accuracy when tested on holdout and independent test sets. TALLSorts is publicly available for use and will be constantly updated as the field of T-ALL classification further develops.

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.

Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy, and implementation of risk-adapted therapy has been instrumental in the dramatic improvements in clinical outcomes. A key to risk-adapted therapies includes the identification of genomic features of individual tumors, including chromosome number (for hyper- and hypodiploidy) and gene fusions, notably ETV6-RUNX1, TCF3-PBX1, and BCR-ABL1 in B-cell ALL (B-ALL). RNA-sequencing (RNA-seq) of large ALL cohorts has expanded the number of recurrent gene fusions recognized as drivers in ALL, and identification of these new entities will contribute to refining ALL risk stratification. We used RNA-seq on 126 ALL patients from our clinical service to test the utility of including RNA-seq in standard-of-care diagnostic pipelines to detect gene rearrangements and IKZF1 deletions. RNA-seq identified 86% of rearrangements detected by standard-of-care diagnostics. KMT2A (MLL) rearrangements, although usually identified, were the most commonly missed by RNA-seq as a result of low expression. RNA-seq identified rearrangements that were not detected by standard-of-care testing in 9 patients. These were found in patients who were not classifiable using standard molecular assessment. We developed an approach to detect the most common IKZF1 deletion from RNA-seq data and validated this using an RQ-PCR assay. We applied an expression classifier to identify Philadelphia chromosome–like B-ALL patients. T-ALL proved a rich source of novel gene fusions, which have clinical implications or provide insights into disease biology. Our experience shows that RNA-seq can be implemented within an individual clinical service to enhance the current molecular diagnostic risk classification of ALL.

B-cell acute lymphoblastic leukemia (B-ALL) is the most common childhood cancer. Subtypes within B-ALL are distinguished by characteristic structural variants and mutations, which in some instances strongly correlate with responses to treatment. The World Health Organisation (WHO) recognises seven distinct classifications, or subtypes, as of 2016. However, recent studies have demonstrated that B-ALL can be segmented into 23 subtypes based on a combination of genomic features and gene expression profiles. A method to identify a patient’s subtype would have clear clinical utility. Despite this, no publically available classification methods using RNA-Seq exist for this purpose.Here we present ALLSorts: a publicly available method that uses RNA-Seq data to classify B-ALL samples to 18 known subtypes and five meta-subtypes. ALLSorts is the result of a hierarchical supervised machine learning algorithm applied to a training set of 1223 B-ALL samples aggregated from multiple cohorts. Validation revealed that ALLSorts can accurately attribute samples to subtypes and can attribute multiple subtypes to a sample. Furthermore, when applied to both paediatric and adult cohorts, ALLSorts was able to classify previously undefined samples into subtypes.ALLSorts is available and documented on GitHub (https://github.com/Oshlack/AllSorts/).Key PointsALLSorts is a gene expression classifier for B-cell acute lymphoblastic leukemia, which predicts 18 distinct genomic subtypes - including those designated by the World Health Organisation (WHO) and provisional entities.Trained and validated on over 2300 B-ALL samples, representing each subtype and a variety of clinical features.Correctly identified subtypes in 91% of cases in a held-out dataset and between 82-93% across a newly combined cohort of paediatric and adult samples.ALLSorts assigned subtypes to samples with previously unknown driver events.ALLsorts is an accurate, comprehensive and freely available classification tool that distinguishes subtypes of B-cell acute lymphoblastic leukemia from RNA-sequencing.

All nephrons in the mammalian kidney arise from a transient nephron progenitor population that is lost close to the time of birth. The generation of new nephron progenitors and their maintenance in culture are central to the success of kidney regenerative strategies. Using a lentiviral screening approach, we previously generated a human induced nephron progenitor-like state in vitro using a pool of six transcription factors. Here, we sought to develop a more efficient approach for direct reprogramming of human cells that could be applied in vivo. PiggyBac transposons are a non-viral integrating gene delivery system that is suitable for in vivo use and allows for simultaneous delivery of multiple genes. Using an inducible piggyBac transposon system, we optimized a protocol for the direct reprogramming of HK2 cells to induced nephron progenitor-like cells with expression of only 3 transcription factors (SNAI2, EYA1, and SIX1). Culture in conditions supportive of the nephron progenitor state further increased the expression of nephron progenitor genes. The refined protocol was then applied to primary human renal epithelial cells, which integrated into developing nephron structures in vitro and in vivo. Such inducible reprogramming to nephron progenitor-like cells could facilitate direct cellular reprogramming for kidney regeneration.

Metal toxicity is a common problem in crop species worldwide. Some metals are naturally toxic, whereas others such as manganese (Mn) are essential micro-nutrients for plant growth but can become toxic when in excess. Changes in the composition of the xylem sap, which is the main pathway for ion transport within the plant, is therefore vital to understanding the plant&rsquo, s response(s) to metal toxicity. In this study we have assessed the effects of exposure of tomato roots to excess Mn on the protein profile of the xylem sap, using a shotgun proteomics approach. Plants were grown in nutrient solution using 4.6 and 300 &micro, M MnCl2 as control and excess Mn treatments, respectively. This approach yielded 668 proteins reliably identified and quantified. Excess Mn caused statistically significant (at p &le, 0.05) and biologically relevant changes in relative abundance (&ge, 2-fold increases or &ge, 50% decreases) in 322 proteins, with 82% of them predicted to be secretory using three different prediction tools, with more decreasing than increasing (181 and 82, respectively), suggesting that this metal stress causes an overall deactivation of metabolic pathways. Processes most affected by excess Mn were in the oxido-reductase, polysaccharide and protein metabolism classes. Excess Mn induced changes in hydrolases and peroxidases involved in cell wall degradation and lignin formation, respectively, consistent with the existence of alterations in the cell wall. Protein turnover was also affected, as indicated by the decrease in proteolytic enzymes and protein synthesis-related proteins. Excess Mn modified the redox environment of the xylem sap, with changes in the abundance of oxido-reductase and defense protein classes indicating a stress scenario. Finally, results indicate that excess Mn decreased the amounts of proteins associated with several signaling pathways, including fasciclin-like arabinogalactan-proteins and lipids, as well as proteases, which may be involved in the release of signaling peptides and protein maturation. The comparison of the proteins changing in abundance in xylem sap and roots indicate the existence of tissue-specific and systemic responses to excess Mn. Data are available via ProteomeXchange with identifier PXD021973.

Metal toxicity is a common problem in crop species worldwide. Some metals are naturally toxic, whereas others such as manganese (Mn) are essential micro-nutrients for plant growth but can become toxic when in excess. Changes in the composition of the xylem sap, which is the main pathway for ion transport within the plant, is therefore vital to understanding the plant’s response(s) to metal toxicity. In this study we have assessed the effects of exposure of tomato roots to excess Mn on the protein profile of the xylem sap, using a shotgun proteomics approach. Plants were grown in nutrient solution using 4.6 and 300 µM MnCl2 as control and excess Mn treatments, respectively. This approach yielded 668 proteins reliably identified and quantified. Excess Mn caused statistically significant (at p ≤ 0.05) and biologically relevant changes in relative abundance (≥2-fold increases or ≥50% decreases) in 322 proteins, with 82% of them predicted to be secretory using three different prediction tools, with more decreasing than increasing (181 and 82, respectively), suggesting that this metal stress causes an overall deactivation of metabolic pathways. Processes most affected by excess Mn were in the oxido-reductase, polysaccharide and protein metabolism classes. Excess Mn induced changes in hydrolases and peroxidases involved in cell wall degradation and lignin formation, respectively, consistent with the existence of alterations in the cell wall. Protein turnover was also affected, as indicated by the decrease in proteolytic enzymes and protein synthesis-related proteins. Excess Mn modified the redox environment of the xylem sap, with changes in the abundance of oxido-reductase and defense protein classes indicating a stress scenario. Finally, results indicate that excess Mn decreased the amounts of proteins associated with several signaling pathways, including fasciclin-like arabinogalactan-proteins and lipids, as well as proteases, which may be involved in the release of signaling peptides and protein maturation. The comparison of the proteins changing in abundance in xylem sap and roots indicate the existence of tissue-specific and systemic responses to excess Mn. Data are available via ProteomeXchange with identifier PXD021973.

Intrinsically disordered proteins (IDPs) are functional proteins that lack a well-defined three-dimensional structure. The study of IDPs is a rapidly growing area as the crucial biological functions of more of these proteins are uncovered. In plants, IDPs are implicated in plant stress responses, signaling, and regulatory processes. A superfamily of cell wall proteins, the hydroxyproline-rich glycoproteins (HRGPs), have characteristic features of IDPs. Their protein backbones are rich in the disordering amino acid proline, they contain repeated sequence motifs and extensive posttranslational modifications (glycosylation), and they have been implicated in many biological functions. HRGPs are evolutionarily ancient, having been isolated from the protein-rich walls of chlorophyte algae to the cellulose-rich walls of embryophytes. Examination of HRGPs in a range of plant species should provide valuable insights into how they have evolved. Commonly divided into the arabinogalactan proteins, extensins, and proline-rich proteins, in reality, a continuum of structures exists within this diverse and heterogenous superfamily. An inability to accurately classify HRGPs leads to inconsistent gene ontologies limiting the identification of HRGP classes in existing and emerging omics data sets. We present a novel and robust motif and amino acid bias (MAAB) bioinformatics pipeline to classify HRGPs into 23 descriptive subclasses. Validation of MAAB was achieved using available genomic resources and then applied to the 1000 Plants transcriptome project (www.onekp.com) data set. Significant improvement in the detection of HRGPs using multiple-k-mer transcriptome assembly methodology was observed. The MAAB pipeline is readily adaptable and can be modified to optimize the recovery of IDPs from other organisms.

Background Bioinformatics software tools are often created ad hoc, frequently by people without extensive training in software development. In particular, for beginners, the barrier to entry in bioinformatics software development is high, especially if they want to adopt good programming practices. Even experienced developers do not always follow best practices. This results in the proliferation of poorer-quality bioinformatics software, leading to limited scalability and inefficient use of resources; lack of reproducibility, usability, adaptability, and interoperability; and erroneous or inaccurate results. Findings We have developed Bionitio, a tool that automates the process of starting new bioinformatics software projects following recommended best practices. With a single command, the user can create a new well-structured project in 1 of 12 programming languages. The resulting software is functional, carrying out a prototypical bioinformatics task, and thus serves as both a working example and a template for building new tools. Key features include command-line argument parsing, error handling, progress logging, defined exit status values, a test suite, a version number, standardized building and packaging, user documentation, code documentation, a standard open source software license, software revision control, and containerization. Conclusions Bionitio serves as a learning aid for beginner-to-intermediate bioinformatics programmers and provides an excellent starting point for new projects. This helps developers adopt good programming practices from the beginning of a project and encourages high-quality tools to be developed more rapidly. This also benefits users because tools are more easily installed and consistent in their usage. Bionitio is released as open source software under the MIT License and is available at https://github.com/bionitio-team/bionitio.

Mixed-linkage (1,3;1,4)-β-glucan (MLG), an abundant cell wall polysaccharide in the Poaceae, has been detected in ascomycetes, algae, and seedless vascular plants, but not in eudicots. Although MLG has not been reported in bryophytes, a predicted glycosyltransferase from the moss Physcomitrella patens (Pp3c12_24670) is similar to a bona fide ascomycete MLG synthase. We tested whether Pp3c12_24670 encodes an MLG synthase by expressing it in wild tobacco (Nicotiana benthamiana) and testing for release of diagnostic oligosaccharides from the cell walls by either lichenase or (1,4)-β-glucan endohydrolase. Lichenase, an MLG-specific endohydrolase, showed no activity against cell walls from transformed N. benthamiana, but (1,4)-β-glucan endohydrolase released oligosaccharides that were distinct from oligosaccharides released from MLG by this enzyme. Further analysis revealed that these oligosaccharides were derived from a novel unbranched, unsubstituted arabinoglucan (AGlc) polysaccharide. We identified sequences similar to the P. patens AGlc synthase from algae, bryophytes, lycophytes, and monilophytes, raising the possibility that other early divergent plants synthesize AGlc. Similarity of P. patens AGlc synthase to MLG synthases from ascomycetes, but not those from Poaceae, suggests that AGlc and MLG have a common evolutionary history that includes loss in seed plants, followed by a more recent independent origin of MLG within the monocots.

Short tandem repeat (STR) expansions have been identified as the causal DNA mutation in dozens of Mendelian diseases. Historically, pathogenic STR expansions could only be detected by single locus techniques, such as PCR and electrophoresis. The ability to use short read sequencing data to screen for STR expansions has the potential to reduce both the time and cost to reaching diagnosis and enable the discovery of new causal STR loci. Most existing tools detect STR variation within the read length, and so are unable to detect the majority of pathogenic expansions. Those tools that can detect large expansions are limited to a set of known disease loci and as yet no new disease causing STR expansions have been identified with high-throughput sequencing technologies.Here we address this by presenting STRetch, a new genome-wide method to detect STR expansions at all loci across the human genome. We demonstrate the use of STRetch for detecting pathogenic STR expansions in short-read whole genome sequencing data with a very low false discovery rate. We further demonstrate the application of STRetch to solve cases of patients with undiagnosed disease and apply STRetch to the analysis of 97 whole genomes to reveal variation at STR loci. STRetch assesses expansions at all STR loci in the genome and allows screening for novel disease-causing STRs.STRetch is open source software, available fromgithub.com/Oshlack/STRetch.

The carbohydrate-rich cell walls of land plants and algae have been the focus of much interest given the value of cell wall-based products to our current and future economies. Hydroxyproline-rich glycoproteins (HRGPs), a major group of wall glycoproteins, play important roles in plant growth and development, yet little is known about how they have evolved in parallel with the polysaccharide components of walls. We investigate the origins and evolution of the HRGP superfamily, which is commonly divided into three major multigene families: the arabinogalactan proteins (AGPs), extensins (EXTs), and proline-rich proteins. Using motif and amino acid bias, a newly developed bioinformatics pipeline, we identified HRGPs in sequences from the 1000 Plants transcriptome project (www.onekp.com). Our analyses provide new insights into the evolution of HRGPs across major evolutionary milestones, including the transition to land and the early radiation of angiosperms. Significantly, data mining reveals the origin of glycosylphosphatidylinositol (GPI)-anchored AGPs in green algae and a 3- to 4-fold increase in GPI-AGPs in liverworts and mosses. The first detection of cross-linking (CL)-EXTs is observed in bryophytes, which suggests that CL-EXTs arose though the juxtaposition of preexisting SPn EXT glycomotifs with refined Y-based motifs. We also detected the loss of CL-EXT in a few lineages, including the grass family (Poaceae), that have a cell wall composition distinct from other monocots and eudicots. A key challenge in HRGP research is tracking individual HRGPs throughout evolution. Using the 1000 Plants output, we were able to find putative orthologs of Arabidopsis pollen-specific GPI-AGPs in basal eudicots.

Garden asparagus (Asparagus officinalis L.) is a commercially important crop species utilized for its excellent source of vitamins, minerals and dietary fiber. However, after harvest the tissue hardens and its quality rapidly deteriorates because spear cell walls become rigidified due to lignification and substantial increases in heteroxylan content. This latter observation prompted us to investigate the in vitro xylan xylosyltransferase (XylT) activity in asparagus. The current model system for studying heteroxylan biosynthesis, Arabidopsis, whilst a powerful genetic system, displays relatively low xylan XylT activity in in vitro microsomal preparations compared with garden asparagus therefore hampering our ability to study the molecular mechanism(s) of heteroxylan assembly. Here, we analyzed physiological and biochemical changes of garden asparagus spears stored at 4 °C after harvest and detected a high level of xylan XylT activity that accounts for this increased heteroxylan. The xylan XylT catalytic activity is at least thirteen-fold higher than that reported for previously published species, including Arabidopsis and grasses. A biochemical assay was optimized and up to seven successive Xyl residues were incorporated to extend the xylotetraose (Xyl4) acceptor backbone. To further elucidate the xylan biosynthesis mechanism, we used RNA-seq to generate an Asparagus reference transcriptome and identified five putative xylan biosynthetic genes (AoIRX9, AoIRX9-L, AoIRX10, AoIRX14_A, AoIRX14_B) with AoIRX9 having an expression profile that is distinct from the other genes. We propose that Asparagus provides an ideal biochemical system to investigate the biochemical aspects of heteroxylan biosynthesis and also offers the additional benefit of being able to study the lignification process during plant stem maturation.