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Additional file 4: Figure S1. Auxiliary summaries from the survey responses. Simulator used, secondary measures assessed, language of tools assessed.

Computational methods represent the lifeblood of modern molecular biology. Benchmarking is important for all methods, but with a focus here on computational methods, benchmarking is critical to dissect important steps of analysis pipelines, formally assess performance across common situations as well as edge cases, and ultimately guide users on what tools to use. Benchmarking can also be important for community building and advancing methods in a principled way. We conducted a meta-analysis of recent single-cell benchmarks to summarize the scope, extensibility, and neutrality, as well as technical features and whether best practices in open data and reproducible research were followed. The results highlight that while benchmarks often make code available and are in principle reproducible, they remain difficult to extend, for example, as new methods and new ways to assess methods emerge. In addition, embracing containerization and workflow systems would enhance reusability of intermediate benchmarking results, thus also driving wider adoption., Genome Biology, 24, ISSN:1474-760X

Additional file 5. Review history.

Aggregation of the RNA-binding protein TDP-43 is the main common neuropathological feature of TDP-43 proteinopathies. In physiological conditions, TDP-43 is predominantly nuclear and contained in biomolecular condensates formed via liquid-liquid phase separation (LLPS). However, in disease, TDP-43 is depleted from these compartments and forms cytoplasmic or, sometimes, intranuclear inclusions. How TDP-43 transitions from physiological to pathological states remains poorly understood. Here, we show that self-oligomerization and RNA binding cooperatively govern TDP-43 stability, functionality, LLPS and cellular localization. Importantly, our data reveal that TDP-43 oligomerization is connected to, and conformationally modulated by, RNA binding. Mimicking the impaired proteasomal activity observed in patients, we found that TDP-43 forms nuclear aggregates via LLPS and cytoplasmic aggregates via aggresome formation. The favored aggregation pathway depended on the TDP-43 state –monomeric/oligomeric, RNA-bound/-unbound– and the subcellular environment –nucleus/cytoplasm. Our work unravels the origins of heterogeneous pathological species occurring in TDP-43 proteinopathies.

Mammalian de novo DNA methyltransferases (DNMT) are responsible for the establishment of cell-type-specific DNA methylation in healthy and diseased tissues. Through genome-wide analysis of de novo methylation activity in murine stem cells we uncover that DNMT3A prefers to methylate CpGs followed by cytosines or thymines, while DNMT3B predominantly methylates CpGs followed by guanines or adenines. These signatures are further observed at non-CpG sites, resembling methylation context observed in specialised cell types, including neurons and oocytes. We further show that these preferences result from structural differences in the catalytic domains of the two de novo DNMTs and are not a consequence of differential recruitment to the genome. Molecular dynamics simulations suggest that, in case of human DNMT3A, the preference is due to favourable polar interactions between the flexible Arg836 side chain and the guanine that base-pairs with the cytosine following the CpG. By exchanging arginine to a lysine, the corresponding side chain in DNMT3B, the sequence preference is reversed, confirming the requirement for arginine at this position. This context-dependent enzymatic activity provides additional insights into the complex regulation of DNA methylation patterns.

Mammaliande novoDNA methyltransferases (DNMT) are responsible for the establishment of cell-type-specific DNA methylation in healthy and diseased tissues. Through genome-wide analysis ofde novomethylation activity in murine stem cells we uncover that DNMT3A prefers to methylate CpGs followed by cytosines or thymines, while DNMT3B predominantly methylates CpGs followed by guanines or adenines. These signatures are further observed at non-CpG sites, resembling methylation context observed in specialised cell types, including neurons and oocytes. We further show that these preferences are not mediated by the differential recruitment of the twode novoDNMTs to the genome but are resulting from structural differences in their catalytic domains. Molecular dynamics simulations suggest that, in case of DNMT3A, the preference is due to favourable polar interactions between the flexible Arg836 side chain and the guanine that base-pairs with the cytosine following the CpG. This context-dependentde novoDNA methylation provides additional insights into the complex regulation of methylation patterns in different cell types.

We addressed the precursor role of aging-like spontaneous promoter DNA hypermethylation in initiating tumorigenesis. Using mouse colon-derived organoids, we show that promoter hypermethylation spontaneously arises in cells mimicking the human aging-like phenotype. The silenced genes activate the Wnt pathway, causing a stem-like state and differentiation defects. These changes render aged organoids profoundly more sensitive than young ones to transformation by Braf(V600E), producing the typical human proximal BRAF(V600E)- driven colon adenocarcinomas characterized by extensive, abnormal gene-promoter CpG-island methylation, or the methylator phenotype (CIMP). Conversely, CRISPR-mediated simultaneous inactivation of a panel of the silenced genes markedly sensitizes to Braf(V600E)-induced transformation. Our studies tightly link aging-like epigenetic abnormalities to intestinal cell fate changes and predisposition to oncogene-driven colon tumorigenesis.

Cancer cells exhibit multiple epigenetic changes with prominent local DNA hypermethylation and widespread hypomethylation affecting large chromosomal domains. Epigenome studies often disregard the study of repeat elements owing to technical complexity and their undefined role in genome regulation. We have developed NSUMA (Next-generation Sequencing of UnMethylated Alu), a cost-effective approach allowing the unambiguous interrogation of DNA methylation in more than 130,000 individual Alu elements, the most abundant retrotransposon in the human genome. DNA methylation profiles of Alu repeats have been analyzed in colon cancers and normal tissues using NSUMA and whole-genome bisulfite sequencing. Normal cells show a low proportion of unmethylated Alu (1%–4%) that may increase up to 10-fold in cancer cells. In normal cells, unmethylated Alu elements tend to locate in the vicinity of functionally rich regions and display epigenetic features consistent with a direct impact on genome regulation. In cancer cells, Alu repeats are more resistant to hypomethylation than other retroelements. Genome segmentation based on high/low rates of Alu hypomethylation allows the identification of genomic compartments with differential genetic, epigenetic, and transcriptomic features. Alu hypomethylated regions show low transcriptional activity, late DNA replication, and its extent is associated with higher chromosomal instability. Our analysis demonstrates that Alu retroelements contribute to define the epigenetic landscape of normal and cancer cells and provides a unique resource on the epigenetic dynamics of a principal, but largely unexplored, component of the primate genome.

Summary DNA methylation dynamics is intrinsically interconnected with processes underlying the malignant properties of cancer cells. By applying network-based approaches in two series of colorectal cancers we dissected the long-range co-methylation structure finding consistent patterns of compartmentalization in both normal and tumor tissues. Large transchromosomal modules showed unique regulatory signatures and coalesced into a structured network and allowing simple patient stratification. Normal-tumor comparison revealed substantial remodeling of specific modules and migration of subsets of co-methylating sites denoted by functional aggregates, pointing out potential sources of epigenetic and phenotypic variability. We conclude that DNA methylation dynamics architecture embodies interpretable information that can be used as a proxy of the drivers and the phenotypes of malignant transformation. Significance DNA methylation is a key epigenetic mark directly involved in genome organization and regulation. DNA methylation profiles are variable and are extensively altered in most cancers. We show that DNA methylation variability follows a transchromosomal modular dynamics in both normal and colon cancer cells. The reshaping of the DNA methylation variability network in tumorigenesis exposes genomic and functional associations and points out both the mechanisms and the phenotypes of individual tumors. This information may be used for patient stratification and identification of disrupted pathways and therapeutic targets. Highlights DNA methylation variability displays a modular architecture in normal and cancer. Coordinated transchromosomal variations supersede regional DNA methylation dynamics. Co-methylation network modularity evinces functional and structural features. Epigenetic rewiring can be used as patient stratifier.

Papillary thyroid cancer (PTC) is the most common type of thyroid cancer. Unlike most cancers, its incidence has dramatically increased in the last decades mainly due to increased diagnosis of indolent PTCs. Adequate risk stratification is crucial to avoid the over-treatment of low-risk patients, as well as the under-treatment of high-risk patients, but the currently available markers are still insufficient. Kallikreins (KLKs) are emergent biomarkers in cancer, but their involvement in PTC is unknown.This study analyzed DNA methylation (HumanMethylation arrays) and gene expression (RNA-Seq) of KLKs, BRAF and RAS mutations, and clinical data from four published thyroid cancer data sets including normal and tumor tissues (n = 73, n = 475, n = 20, and n = 82) as discovery, training, and validation series. The C4.5 classification algorithm was used to generate a decision tree. Disease-free survival was estimated using Kaplan-Meier and Cox approaches. Specific analyses were performed using real-time polymerase chain reaction and immunohistochemistry.The entire KLK family was deregulated in PTC, displaying a specific epigenetic and transcriptional profile strongly associated with BRAFThe KLK algorithm could lead to a new clinically applicable strategy with important implications for the risk stratification of PTC and the management of patients.

The Cancer Genome Atlas (TCGA) epigenome data includes the DNA methylation status of tumor and normal tissues of large cohorts for dozens of cancer types. Due to the moderately large data sizes, retrieving and analyzing them requires basic programming skills. Simple data browsing (e.g., candidate gene search) is hampered by the scarcity of easy-to-use data browsers addressed to the broad community of biomedical researchers. We propose a new visualization method depicting the overall DNA methylation status at each TCGA cohort while emphasizing its heterogeneity, thus facilitating the evaluation of the cohort variability and the normal versus tumor differences. Implemented as a trackhub integrated to the University of California Santa Cruz (UCSC) genome browser, it can be easily added to any genome-wide annotation layer.To exemplify the trackhub usage we evaluate local DNA methylation boundaries, the aberrant DNA methylation of a CpG island located at the estrogen receptor 1 (ESR1) in breast and colon cancer, and the hypermethylation of the Homeobox HOXA gene cluster and the EN1 gene in multiple cancer types. The DNA methylation pancancer trackhub is freely available at http://maplab.cat/tcga_450k_trackhub .

The Cancer Genome Atlas (TCGA) epigenome data includes the DNA methylation status of tumor and normal tissues of large cohorts for dozens of cancer types. Due to the moderately large data sizes, retrieving and analyzing them requires basic programming skills. Simple data browsing (e.g., candidate gene search) is hampered by the scarcity of easy-to-use data browsers addressed to the broad community of biomedical researchers. We propose a new visualization method depicting the overall DNA methylation status at each TCGA cohort while emphasizing its heterogeneity, thus facilitating the evaluation of the cohort variability and the normal versus tumor differences. Implemented as a trackhub integrated to the University of California Santa Cruz (UCSC) genome browser, it can be easily added to any genome-wide annotation layer.To exemplify the trackhub usage we evaluate local DNA methylation boundaries, the aberrant DNA methylation of a CpG island located at the estrogen receptor 1 (ESR1) in breast and colon cancer, and the hypermethylation of the Homeobox HOXA gene cluster and the EN1 gene in multiple cancer types. The DNA methylation pancancer trackhub is freely available at http://maplab.cat/tcga_450k_trackhub .

Background Genomic datasets accompanying scientific publications show a surprisingly high rate of gene name corruption. This error is generated when files and tables are imported into Microsoft Excel and certain gene symbols are automatically converted into dates. Results We have developed Truke, a fexible Web tool to detect, tag and fix, if possible, such misconversions. Aside, Truke is language and regional locale-aware, providing file format customization (decimal symbol, field sepator, etc.) following user’s preferences. Conclusions Truke is a data format conversion tool with a unique corrupted gene symbol detection utility. Truke is freely available without registration at http://maplab.cat/truke. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3631-8) contains supplementary material, which is available to authorized users.

Summary Chainy is a cross-platform web tool providing systematic pipelines and steady criteria to process real-time PCR data, including the calculation of efficiencies from raw data by kinetic methods, evaluation of the suitability of multiple references, standardized normalization using one or more references, and group-wise relative quantification statistical testing. We illustrate the utility of Chainy for differential expression and chromatin immunoprecipitation enrichment (ChIP-QPCR) analysis. Availability and Implementation Chainy is open source and freely available at http://maplab.cat/chainy Supplementary information Supplementary data are available at Bioinformatics online.

Hypomethylation of DNA is a hallmark of cancer and its analysis as tumor biomarker has been proposed, but its determination in clinical settings is hampered by lack of standardized methodologies. Here, we present QUAlu (Quantification of Unmethylated Alu), a new technique to estimate the Percentage of UnMethylated Alu (PUMA) as a surrogate for global hypomethylation. QUAlu consists in the measurement by qPCR of Alu repeats after digestion of genomic DNA with isoschizomers with differential sensitivity to DNA methylation. QUAlu performance has been evaluated for reproducibility, trueness and specificity, and validated by deep sequencing. As a proof of use, QUAlu has been applied to a broad variety of pathological examination specimens covering five cancer types. Major findings of the preliminary application of QUAlu to clinical samples include: (1) all normal tissues displayed similar PUMA; (2) tumors showed variable PUMA with the highest levels in lung and colon and the lowest in thyroid cancer; (3) stools from colon cancer patients presented higher PUMA than those from control individuals; (4) lung squamous cell carcinomas showed higher PUMA than lung adenocarcinomas, and an increasing hypomethylation trend associated with smoking habits. In conclusion, QUAlu is a simple and robust method to determine Alu hypomethylation in human biospecimens and may be easily implemented in research and clinical settings. RB was supported by a FPI fellowship from Ministerio de Economía y Competitividad. AD-V was supported in part by a contract PTC2011-1091 from Ministerio de Economía y Competitividad. This work was supported by grants from FEDER, the Ministerio de Economía y Competitividad (SAF2011/23638 to MAP), the Instituto de Salud Carlos III (FIS PI11/02421 to JR, FIS PI11/01359 and FIS PI14/00240 to MR, FIS PI14/00308 to MJ, FIS PI12/00511 to MP), and Fundació Olga Torres (to MJ).