cfDNA UniFlow: a unified preprocessing pipeline for cell-free DNA data from liquid biopsies.

Background: Cell-free DNA (cfDNA), a broadly applicable biomarker commonly sourced from urine or blood, is extensively used for research and diagnostic applications. In various settings, genetic and epigenetic information is derived from cfDNA. However, a unified framework for its processing is lacking, limiting the universal application of innovative analysis strategies and the joining of data sets.

LBFextract: Unveiling transcription factor dynamics from liquid biopsy data.

Motivation: The analysis of circulating cell-free DNA (cfDNA) holds immense promise as a non-invasive diagnostic tool across various human conditions. However, extracting biological insights from cfDNA fragments entails navigating complex and diverse bioinformatics methods, encompassing not only DNA sequence variation, but also epigenetic characteristics like nucleosome footprints, fragment length, and methylation patterns.

Results: We introduce Liquid Biopsy Feature extract (LBFextract), a comprehensive package designed to streamline feature extraction from cfDNA sequencing data, with the aim of enhancing the reproducibility and comparability of liquid biopsy studies.

Massively parallel jumping assay decodes retrotransposition activity.

The human genome contains millions of retrotransposons, several of which could become active due to somatic mutations having phenotypic consequences, including disease. However, it is not thoroughly understood how nucleotide changes in retrotransposons affect their jumping activity. Here, we developed a novel massively parallel jumping assay (MPJA) that can test the jumping potential of thousands of transposons .

STIGMA: Single-cell tissue-specific gene prioritization using machine learning.

Clinical exome and genome sequencing have revolutionized the understanding of human disease genetics. Yet many genes remain functionally uncharacterized, complicating the establishment of causal disease links for genetic variants. While several scoring methods have been devised to prioritize these candidate genes, these methods fall short of capturing the expression heterogeneity across cell subpopulations within tissues.

CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions.

Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of the first methods for the genome-wide prioritization of variants across different molecular functions and has been continuously developed and improved since its original publication. Here, we present our most recent release, CADD v1.

GCparagon: evaluating and correcting GC biases in cell-free DNA at the fragment level.

Analyses of cell-free DNA (cfDNA) are increasingly being employed for various diagnostic and research applications. Many technologies aim to increase resolution, e.g.

Predicting the pathogenicity of missense variants using features derived from AlphaFold2.

Motivation: Missense variants are a frequent class of variation within the coding genome, and some of them cause Mendelian diseases. Despite advances in computational prediction, classifying missense variants into pathogenic or benign remains a major challenge in the context of personalized medicine. Recently, the structure of the human proteome was derived with unprecedented accuracy using the artificial intelligence system AlphaFold2.

The Regulatory Mendelian Mutation score for GRCh38.

Background: Genome sequencing efforts for individuals with rare Mendelian disease have increased the research focus on the noncoding genome and the clinical need for methods that prioritize potentially disease causal noncoding variants. Some tools for assessment of variant pathogenicity as well as annotations are not available for the current human genome build (GRCh38), for which the adoption in databases, software, and pipelines was slow.

Results: Here, we present an updated version of the Regulatory Mendelian Mutation (ReMM) score, retrained on features and variants derived from the GRCh38 genome build.

Systematic assays and resources for the functional annotation of non-coding variants.

Identification of genetic variation in individual genomes is now a routine procedure in human genetic research and diagnostics. For many variants, however, insufficient evidence is available to establish a pathogenic effect, particularly for variants in non-coding regions. Furthermore, the sheer number of candidate variants renders testing in individual assays virtually impossible.

Massively parallel characterization of transcriptional regulatory elements in three diverse human cell types.

The human genome contains millions of candidate -regulatory elements (CREs) with cell-type-specific activities that shape both health and myriad disease states. However, we lack a functional understanding of the sequence features that control the activity and cell-type-specific features of these CREs. Here, we used lentivirus-based massively parallel reporter assays (lentiMPRAs) to test the regulatory activity of over 680,000 sequences, representing a nearly comprehensive set of all annotated CREs among three cell types (HepG2, K562, and WTC11), finding 41.

Aberrant phase separation and nucleolar dysfunction in rare genetic diseases.

Thousands of genetic variants in protein-coding genes have been linked to disease. However, the functional impact of most variants is unknown as they occur within intrinsically disordered protein regions that have poorly defined functions. Intrinsically disordered regions can mediate phase separation and the formation of biomolecular condensates, such as the nucleolus.

Machine learning dissection of human accelerated regions in primate neurodevelopment.

Using machine learning (ML), we interrogated the function of all human-chimpanzee variants in 2,645 human accelerated regions (HARs), finding 43% of HARs have variants with large opposing effects on chromatin state and 14% on neurodevelopmental enhancer activity. This pattern, consistent with compensatory evolution, was confirmed using massively parallel reporter assays in chimpanzee and human neural progenitor cells. The species-specific enhancer activity of HARs was accurately predicted from the presence and absence of transcription factor footprints in each species.

Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques.

Background: Cis-regulatory regions (CRRs) are non-coding regions of the DNA that fine control the spatio-temporal pattern of transcription; they are involved in a wide range of pivotal processes such as the development of specific cell-lines/tissues and the dynamic cell response to physiological stimuli. Recent studies showed that genetic variants occurring in CRRs are strongly correlated with pathogenicity or deleteriousness. Considering the central role of CRRs in the regulation of physiological and pathological conditions, the correct identification of CRRs and of their tissue-specific activity status through Machine Learning methods plays a major role in dissecting the impact of genetic variants on human diseases.

Results of genetic analysis of 11 341 participants enrolled in the My Life, Our Future hemophilia genotyping initiative in the United States.

Background: Hemophilia A (HA) and hemophilia B (HB) are rare inherited bleeding disorders. Although causative genetic variants are clinically relevant, in 2012 only 20% of US patients had been genotyped.

Objectives: My Life, Our Future (MLOF) was a multisector cross-sectional US initiative to improve our understanding of hemophilia through widespread genotyping.

Computational and experimental methods for classifying variants of unknown clinical significance.

The increase in sequencing capacity, reduction in costs, and national and international coordinated efforts have led to the widespread introduction of next-generation sequencing (NGS) technologies in patient care. More generally, human genetics and genomic medicine are gaining importance for more and more patients. Some communities are already discussing the prospect of sequencing each individual's genome at time of birth.

A framework to score the effects of structural variants in health and disease.

Although technological advances improved the identification of structural variants (SVs) in the human genome, their interpretation remains challenging. Several methods utilize individual mechanistic principles like the deletion of coding sequence or 3D genome architecture disruptions. However, a comprehensive tool using the broad spectrum of available annotations is missing.

CADD-Splice-improving genome-wide variant effect prediction using deep learning-derived splice scores.

Background: Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing.

The impact of different negative training data on regulatory sequence predictions.

Regulatory regions, like promoters and enhancers, cover an estimated 5-15% of the human genome. Changes to these sequences are thought to underlie much of human phenotypic variation and a substantial proportion of genetic causes of disease. However, our understanding of their functional encoding in DNA is still very limited.

A systematic evaluation of the design and context dependencies of massively parallel reporter assays.

Massively parallel reporter assays (MPRAs) functionally screen thousands of sequences for regulatory activity in parallel. To date, there are limited studies that systematically compare differences in MPRA design. Here, we screen a library of 2,440 candidate liver enhancers and controls for regulatory activity in HepG2 cells using nine different MPRA designs.

lentiMPRA and MPRAflow for high-throughput functional characterization of gene regulatory elements.

Massively parallel reporter assays (MPRAs) can simultaneously measure the function of thousands of candidate regulatory sequences (CRSs) in a quantitative manner. In this method, CRSs are cloned upstream of a minimal promoter and reporter gene, alongside a unique barcode, and introduced into cells. If the CRS is a functional regulatory element, it will lead to the transcription of the barcode sequence, which is measured via RNA sequencing and normalized for cellular integration via DNA sequencing of the barcode.

HemoMIPs-Automated analysis and result reporting pipeline for targeted sequencing data.

Targeted sequencing of genomic regions is a cost- and time-efficient approach for screening patient cohorts. We present a fast and efficient workflow to analyze highly imbalanced, targeted next-generation sequencing data generated using molecular inversion probe (MIP) capture. Our Snakemake pipeline performs sample demultiplexing, overlap paired-end merging, alignment, MIP-arm trimming, variant calling, coverage analysis and report generation.

Concurrent genome and epigenome editing by CRISPR-mediated sequence replacement.

Background: Recent advances in genome editing have facilitated the direct manipulation of not only the genome, but also the epigenome. Genome editing is typically performed by introducing a single CRISPR/Cas9-mediated double-strand break (DSB), followed by non-homologous end joining (NHEJ)- or homology-directed repair-mediated repair. Epigenome editing, and in particular methylation of CpG dinucleotides, can be performed using catalytically inactive Cas9 (dCas9) fused to a methyltransferase domain.

Saturation mutagenesis of twenty disease-associated regulatory elements at single base-pair resolution.

The majority of common variants associated with common diseases, as well as an unknown proportion of causal mutations for rare diseases, fall in noncoding regions of the genome. Although catalogs of noncoding regulatory elements are steadily improving, we have a limited understanding of the functional effects of mutations within them. Here, we perform saturation mutagenesis in conjunction with massively parallel reporter assays on 20 disease-associated gene promoters and enhancers, generating functional measurements for over 30,000 single nucleotide substitutions and deletions.