数据与 AI技能

Process sample-separated 16S rRNA or ITS FASTQ files with DADA2 to create an ASV table, which distinguishes exact sequence variants. Covers quality filtering, error learning, read merging, chimera removal, and read counts at each processing stage.

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• Creating an ASV table from sample-separated 16S FASTQ files
• Processing ITS reads after primer removal
• Reviewing read quality and adjusting trimming settings

Process PacBio Iso-Seq data to discover and quantify full-length transcript variants, called isoforms. Follow read cleanup, clustering, genome alignment, and quality checks, then classify known and novel variants and examine differences in isoform usage.

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• Discovering full-length transcript variants in Iso-Seq data
• Classifying known and novel splice variants
• Checking isoform quality and filtering artifacts

Map measured metabolites to biological pathways and identify pathways represented more than expected. Use enrichment tests and pathway structure analysis to interpret results, display metabolite values on pathway diagrams, and export sorted findings for further review.

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• Connecting a metabolite list to biological pathways
• Finding pathways associated with metabolite changes
• Displaying measured values on KEGG pathway diagrams

Analyze serial liquid biopsy measurements to track ctDNA, tumor-derived DNA in blood. Summarize tumor fraction trends, mutation clearance, and rises after the lowest measurement through plots and structured reports using specified response thresholds.

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• Plot ctDNA trends during treatment
• Track mutation decline and clearance
• Flag tumor fraction rises after the lowest measurement

Analyze lipid measurements from LC-MS, a method combining separation and mass measurement. Organize lipid classes and chain composition, compare abundance between conditions, visualize changes, and relate results to lipid pathways using lipidr and annotation resources.

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• Analyze lipid measurements exported from LipidSearch or MS-DIAL
• Find lipids whose abundance differs between conditions
• Compare lipid classes, chain lengths, and saturation patterns

Analyze how cell types are arranged in imaging mass cytometry data, a method for measuring many cellular markers in tissue images. Build maps of neighboring cells, test spatial enrichment, and export plots and tables describing cell proximity patterns.

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• Test which cell types occur near each other more often
• Compare cell type co-occurrence across distances
• Find regions where CD8 T cells and tumor cells are close

Classify sequencing reads from mixed biological samples with Kraken2 to identify represented organisms. Covers reference database setup, paired reads, filtering settings, separating unclassified reads, and summarizing species or genus results before abundance estimation.

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• Identifying organisms in metagenomic samples
• Summarizing classifications by species or genus
• Separating classified and unclassified reads

Assign candidate compounds to signals detected in untargeted metabolomics. Match mass measurements and fragmentation spectra against reference databases, check retention time, and organize annotations with confidence levels for export as a compound identification report.

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• Finding metabolite candidates from measured mass values
• Comparing MS/MS spectra with reference libraries
• Checking compound candidates against retention time

Analyze metabolite measurements in R to find differences between experimental groups and visualize sample patterns. Use statistical tests, classification models, and performance measures to rank relevant metabolites and explore potential biomarkers, which are measurable indicators of biological conditions.

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• Finding metabolites that differ between experimental groups
• Exploring sample patterns and outliers with PCA
• Building and evaluating group classification models

Prepare metabolomics measurements for statistical analysis in R. Correct signal drift and differences between measurement batches, handle missing values, normalize intensities, and compare quality before and after correction using reproducibility metrics and sample plots.

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• Correct signal drift using quality control samples
• Adjust differences between measurement batches
• Filter and fill missing measurements

Use Clair3 to detect inherited single-base changes and small insertions or deletions from aligned ONT or PacBio HiFi long reads. Select platform models, restrict analysis to target regions, and filter variant files by quality.

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• Detect variants from aligned ONT or PacBio HiFi reads
• Limit variant detection to regions specified in a BED file
• Filter variant results by quality or variant type

Analyze DNA methylation data in R with methylKit. Import Bismark files, filter and normalize read coverage, compare samples, and identify methylation differences at individual DNA sites or across fixed genomic regions.

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• Filter and normalize Bismark methylation data
• Compare methylation between control and treatment groups
• Examine sample relationships with correlation and PCA

Identify nuclei and whole cells in preprocessed IMC or MIBI tissue images using Cellpose or Mesmer. Extract marker intensities, cell shapes, and positions, inspect segmentation overlays, and save labeled images and tables for further single-cell analysis.

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• Identifying nuclei and whole cells in tissue images
• Extracting marker intensities for each cell
• Checking cell boundaries with image overlays

Detect chromatin loops, contacts between distant DNA regions, from Hi-C data using cooltools or chromosight. Filter candidates by score, compare conditions, check CTCF binding sites, and export results for further analysis and visualization.

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• Detecting chromatin loops in Hi-C data
• Checking loop anchors against CTCF binding sites
• Finding contacts between promoters and enhancers

Evaluate imaging mass cytometry (IMC) images before analysis. Measure signal relative to noise, compare channels, inspect tissue coverage and acquisition artifacts, and summarize cell segmentation quality across samples in tables and visual reports.

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• Check IMC image quality before analysis
• Investigate noisy channels and unexpected correlations
• Inspect tissue fragmentation and cell segmentation quality

Detect large DNA changes, such as deletions, insertions, and inversions, from aligned ONT or PacBio long reads. Use command-line tools to produce variant files, combine samples or callers, and filter or annotate results.

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• Detect structural variants in ONT or PacBio data
• Jointly analyze structural variants across multiple samples
• Merge results from multiple variant callers

Explore which antigen fragments T-cell receptors may recognize. The skill covers ERGO-II setup, matching receptor sequences against known examples in VDJdb, grouping similar sequences, and summarizing candidate recognition patterns across a receptor collection.

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• Prepare ERGO-II for receptor–antigen recognition predictions
• Match receptor sequences to known epitopes in VDJdb
• Group receptors with similar CDR3 sequences

Align bisulfite sequencing reads to a reference genome with Bismark. Prepare genome indexes, handle single or paired reads, and generate BAM alignment files carrying methylation information for WGBS and RRBS analysis.

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• Aligning WGBS or RRBS reads
• Preparing reference genome indexes
• Processing PBAT or non-directional libraries

Label cell types in imaging mass cytometry images using napari, an image viewer, and marker overlays. Export labeled cells as training data, extend labels to similar cells, and generate image panels for annotation review.

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• Manually labeling cell types in IMC images
• Creating training data for cell classifiers
• Extending manual labels to unlabeled cells

Compare Hi-C matrices, which record contacts between genome regions, across experimental conditions. Calculate contact changes, test differences using biological replicates, and produce comparison maps and tables of contacts with statistically significant changes.

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• Comparing Hi-C contact maps between conditions
• Testing contact differences using biological replicates
• Examining compartment switches and TAD boundary changes

Preprocess LC-MS metabolomics data with MS-DIAL, from detecting peaks and matching them across samples to assigning metabolite candidates. Prepare filtered measurement tables for further analysis in R or Python, or export data for MetaboAnalyst.

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• Preprocessing LC-MS data through the MS-DIAL GUI
• Filtering and transforming MS-DIAL exports in Python
• Preparing measurement tables for MetaboAnalyst

Analyze microbial diversity within and between samples using R. Calculate diversity measures, visualize community differences, and test whether sample groups differ in composition, with checks for sequencing depth and variation within groups.

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• Compare microbial diversity across sample groups
• Visualize differences in community composition
• Check whether sequencing depth is sufficient

Turn Hi-C data, which measures contacts between genome regions, into heatmaps and comparison figures. Overlay domain boundaries and loops, plot contacts from a chosen location, and combine contact maps with genomic annotation tracks.

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• Plot contact maps for a selected genome region
• Overlay domain boundaries and loops
• Compare contact patterns between two samples

Analyze methylation, a chemical marking of DNA, in cell-free DNA samples for cancer detection research. Extract measurements, compare cancer and normal samples, and estimate tissue contributions using reference methylation profiles.

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• Extract methylation measurements from bisulfite sequencing data
• Compare methylation in cancer and normal samples
• Estimate tissue contributions to cell-free DNA