deconvolution

Correcting palindromes in long reads after whole-genome amplification

Pacasus corrects artificial palindromic errors in long reads after whole-genome amplification, improving mapping and assembly results.

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

DeMixT improves transcriptomic deconvolution by estimating component proportions and expression profiles in heterogeneous samples, showing high accuracy in validation and TCGA data.

Accurate prediction of cell composition, age, smoking consumption and infection serostatus based on blood DNA methylation profiles

The paper develops a method to predict blood cell composition, age, smoking, and infection status using DNA methylation profiles, achieving high accuracy with fewer methylation sites than existing methods.

Unsupervised deconvolution of molecular heterogeneity uncovers novel signatures and glia-neuron ratio

The paper investigates the glia-neuron ratio in the brain using unsupervised deconvolution methods on single-cell data from human brains.

Marked point process filter for clusterless and adaptive encoding-decoding of multiunit activity

The paper introduces a Bayesian encoding model for real-time adaptation of neural activity decoding in rats during maze exploration.

Genotype fingerprints enable fast and private comparison of genetic testing results for research and direct-to-consumer applications

Fast and simple comparison of semi-structured data, with emphasis on electronic health records

The paper introduces a fast hashing method for comparing semi-structured data, demonstrating its use on electronic health records for duplicate detection and clustering.

A Bayesian Framework for Estimating Cell Type Composition from DNA Methylation Without the Need for Methylation Reference

This paper introduces a Bayesian method to estimate cell type composition from DNA methylation without requiring a methylation reference, showing it can identify individual cell types rather than just linear combinations.

ENRICH: a fast method to improve the quality of flexible macromolecular reconstructions

ENRICH improves 3D map quality from small, heterogeneous cryo-EM datasets by merging particles across conformations using optical flow.

TranslucentID: Detecting Individuals with High Confidence in Saturated DNA SNP Mixtures

TranslucentID identifies contributors in saturated DNA SNP mixtures by desaturating them using minor allele ratios, improving detection confidence in forensic analysis.

PBHoover and CigarRoller: a method for confident haploid variant calling on Pacific Biosciences data and its application to heterogeneous population analysis

PBHoover and CigarRoller improve haploid variant calling on Pacific Biosciences data, enabling sensitive detection of subpopulations in clinical isolates.

Gene Expression Distribution Deconvolution in Single Cell RNA Sequencing

The paper introduces DESCEND, a method to deconvolve true gene expression distributions from noisy scRNA-seq data, improving estimates of dispersion and burstiness while adjusting for covariates.

CogStack - Experiences Of Deploying IntegratedInformation Retrieval And Extraction Services In A Large National Health Service Foundation Trust Hospital

The paper describes the development and deployment of an open-source information retrieval and extraction system in a UK hospital to improve data flow and governance while supporting clinical and research needs.

Deconvoluting Virome-Wide Antiviral Antibody Profiling Data

The paper introduces AVARDA, a software package for analyzing VirScan data to deconvolute antiviral antibody responses, improving viral encephalitis diagnosis by 62.5% in a cohort.

High Temporal-Resolution Dynamic PET Image Reconstruction Using A New Spatiotemporal Kernel Method

This paper introduces a spatiotemporal kernel method for dynamic PET reconstruction, achieving better noise reduction and performance compared to spatial kernel and conventional ML methods.

Using pseudoalignment and base quality to accurately quantify microbial community composition

This paper introduces Karp, a method for accurately quantifying microbial community composition from pooled DNA samples using pseudoalignment and base quality information.

Specter: linear deconvolution as a new paradigm for targeted analysis of data-independent acquisition mass spectrometry proteomics

Specter improves DIA proteomics by directly deconvoluting spectra using linear algebra, enhancing sensitivity for similar peptides and challenging cases.

Transcriptional deconvolution reveals consistent functional subtypes of pancreatic cancer epithelium and stroma

The paper develops an algorithm to deconvolve bulk tumor gene expression data and identifies consistent functional subtypes in pancreatic cancer epithelium and stroma.

VIGA: a sensitive, precise and automatic de novo VIral Genome Annotator.

VIGA is a de novo viral genome annotator that accurately identifies coding sequences and reduces non-informative annotations in viral (meta)genomics data.

FALCON-meta: a method to infer metagenomic composition of ancient DNA

FALCON-meta infers metagenomic composition from ancient DNA samples using an alignment-free compression-based method, avoiding overestimation and computational inefficiencies.

The Juicebox Assembly Tools module facilitates de novo assembly of mammalian genomes with chromosome-length scaffolds for under $1000

Point-and-click correction of misassemblies on Hi-C heatmaps, paired with the automated 3D-DNA pipeline, gave chromosome-length wombat, opossum and raccoon scaffolds from just short-insert DNA-Seq and in situ Hi-C reads.

SemEHR: A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical Research

SemEHR is a semantic search system for clinical notes that improves data extraction and patient recruitment in healthcare settings.

Reproducibility and repeatability of six high-throughput 16S rDNA sequencing protocols for microbiota profiling

The paper evaluates six 16S rDNA sequencing protocols for microbiota profiling, finding that TS-tailed methods provide the highest reproducibility and repeatability on the HiSeq platform.

Identification of methylation and hydroxymethylation haplotype blocks as distal regulatory elements aids in deconvolution of heterogeneous brain tissues

Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

This paper estimates immune cell content in tumour tissue using single-cell RNA-seq data and demonstrates the importance of tumour-derived reference gene expression profiles for accurate deconvolution.

The Plateau Method for Forensic DNA SNP Mixture Deconvolution

The paper introduces the Plateau method to deconvolve DNA mixtures into individual sub-profiles using SNP data without reference profiles, detecting contributors as low as 1/200 in concentration.

Infino: a Bayesian hierarchical model improves estimates of immune infiltration into tumor microenvironment

Infino improves immune infiltration estimates in tumor microenvironments by using a Bayesian hierarchical model to deconvolve bulk RNA-seq data and enhance accuracy.

Leukocyte counts based on site-specific DNA methylation analysis

The paper develops a method to estimate leukocyte counts using DNA methylation analysis, achieving similar precision to conventional methods with advantages in sample handling.

Technology-independent estimation of cell type composition using differentially methylated regions

The paper introduces a method to estimate cell type composition from DNA methylation data without relying on platform-specific reference profiles.

Leveraging heterogeneity across multiple data sets increases accuracy of cell-mixture deconvolution and reduces biological and technical biases

This paper introduces immunoStates, a basis matrix that reduces biological and technical biases in cell-mixture deconvolution by incorporating diverse disease states and microarray platforms.