数据与 AI技能

Identify microbial taxa whose abundance differs between experimental groups while accounting for data measured as relative proportions. Provides R workflows using ALDEx2, ANCOM-BC2, and MaAsLin2 to filter statistical results, handle additional variables, and visualize differences.

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• Finding microbial taxa that differ between groups
• Analyzing associations while accounting for additional variables
• Checking the robustness of detected differences

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

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

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

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

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

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 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

Screen bacterial genomes and metagenomes for antimicrobial resistance genes using AMRFinderPlus, ResFinder, and CARD/RGI. Compare database results, summarize resistance by drug class, and organize findings across samples for resistance profiling.

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• Screening clinical isolates for resistance genes
• Comparing resistance profiles across surveillance samples
• Measuring resistance gene abundance in metagenomes

Analyze 16S or ITS amplicon data, sequences from amplified marker regions, through the QIIME2 command line. Import reads, remove noise, assign taxonomy, compare microbial diversity and abundance between groups, and retain a record of processing steps.

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• Analyze 16S or ITS data through the QIIME2 command line
• Track processing history for reproducible analysis
• Compare microbial diversity and abundance between groups

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

Calculate concentrations of selected metabolites from MRM/SRM measurements, which track specific molecular signals. Use calibration curves and internal reference compounds, apply dilution corrections, and assess measurement accuracy, variability, detection limits, and quantification limits.

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• Calculate metabolite concentrations from calibration curves
• Normalize signals using internal standards
• Assess accuracy, precision, and detection limits

Normalize CyTOF and high-parameter flow cytometry data using calibration beads, particles with reference signals. Covers signal drift correction, alignment across experimental batches, before-and-after plots, and export of normalized, bead-free FCS files.

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• Correcting signal drift during long CyTOF runs
• Aligning measurement batches with shared reference samples
• Comparing marker distributions before and after normalization

Classify viral sequences into genetic lineages with Nextclade or pangolin, then summarize their distribution and quality checks. Use collection dates to track weekly variant proportions and flag mutations whose frequency has increased between earlier and recent samples.

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• Assign genetic lineages to viral sequences
• Track weekly variant proportions
• Find mutations increasing in frequency

Select sequence records by length, ID, GC content—the proportion of G and C bases—or matching patterns using Biopython. Combine conditions, exclude unwanted records, sample sequences, and save selected records to separate files.

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• Removing sequences shorter than a chosen length
• Extracting sequences from an ID list
• Selecting sequences by GC content or motifs

Find cell populations in flow or mass cytometry data without predefined selection boundaries. Use FlowSOM, Phenograph, and CATALYST to group cells, interpret marker patterns, assign cell type labels, and visualize population proportions across samples.

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• Discover cell populations without predefined gates
• Label cell clusters using marker expression
• Compare cell population proportions across samples

Detect and filter doublets, measurements containing multiple cells, in flow cytometry and CyTOF data. Use scatter signals, DNA measurements, or event length to prepare single-cell data for clustering and quantitative analysis.

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• Remove doublets before clustering
• Select single cells using scatter signals
• Filter CyTOF data using DNA or event length

Detect inherited single-base changes and small insertions or deletions with GATK. The skill covers single-sample analysis, joint analysis across samples, and quality filtering, with workflows for both large cohorts and smaller datasets.

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• Calling germline variants from a single BAM file
• Joint genotyping across multiple samples
• Filtering variants in small datasets

Analyze FASTQ sequencing read quality with Biopython. Summarize quality scores, filter reads, trim low-quality bases, and inspect quality at each position. Convert older FASTQ quality encodings to the standard format when preparing data for further analysis.

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• Filtering FASTQ reads by average or minimum quality
• Trimming low-quality read ends
• Checking quality changes across read positions

Assess pooled CRISPR screen quality before selecting candidate genes. Check guide RNA coverage, read-count balance, agreement between repeated experiments, and recovery of known essential genes, then produce diagnostic plots and a summary of quality checks.

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• Checking screen quality before selecting candidate genes
• Investigating missing guides and uneven read counts
• Comparing agreement between repeated experiments

Analyze pooled CRISPR screens with MAGeCK, from sequencing read counts to ranked genes. Compare enriched or depleted guides to identify candidate essential or resistance genes, visualize results, and examine pathways across simple or multi-condition experiments.

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• Counting sgRNA reads from FASTQ files
• Finding candidate essential or drug-resistance genes
• Analyzing multi-condition or time-course screens

Convert biological sequence files between FASTA, FASTQ, GenBank, and EMBL using Biopython. Handle required annotations, process multiple files, and identify when conversion discards information such as sequence quality scores or annotated biological features.

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• Converting GenBank files to FASTA
• Preparing sequence files for another analysis tool
• Batch converting files in a directory

Create sample-specific FASTA sequences by applying recorded genetic variants to a reference with bcftools consensus. Select haplotypes, representing alternative inherited sequences, mask low-coverage regions, and export coordinate mappings between the reference and resulting sequence.

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• Create a sample-specific sequence from VCF variants
• Export each haplotype as a separate sequence
• Mask regions with insufficient sequencing coverage

Assign bacterial strain types using MLST, which compares multiple genes, and cgMLST, which compares core genome genes. Compare isolate profiles, cluster related samples, and extract selected single-base variants for SNP-based typing.

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• Assigning sequence types to bacterial isolates
• Comparing core genome allele profiles across isolates
• Clustering isolates to investigate potential outbreak groups