Ringi

Map N6-methyladenosine (m6A) RNA modifications at single-nucleotide resolution using miCLIP (Linder 2015), miCLIP2 + m6Aboost machine learning (Kortel 2021), GLORI (Liu 2023, antibody-free chemical conversion), DART-seq (Meyer 2019, APOBEC1-YTH fusion), m6Anet (nanopore direct RNA), or MeRIP-seq with calibration. Use when distinguishing antibody-based from antibody-free m6A detection methods, applying the DRACH motif constraint, reconciling cross-method disagreements (DART 44% in DRACH vs GLORI), or detecting m6Am at the cap.

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Detect single-nucleotide crosslink (CL) sites in CLIP-seq data using truncation patterns (iCLIP/eCLIP CITS), crosslink-induced mutations (HITS-CLIP CIMS deletions, PAR-CLIP T-to-C), or HMM/kernel-density methods (PureCLIP, PARalyzer, CTK). Use when single-nucleotide resolution is required for motif registration (mCross), allele-specific binding (BEAPR), variant-effect prediction, or comparing crosslink chemistry across CLIP variants.

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Corrects the gene-independent copy-number artifact in CRISPR-Cas9 screens (Aguirre 2016 / Munoz 2016 Cancer Discov) where amplified loci appear essential from DNA-damage burden of simultaneous cuts. Covers the gene-independent DNA-damage / G2-arrest mechanism, CRISPRcleanR (Iorio 2018) unsupervised pre-hoc correction, CERES (Meyers 2017) joint CN + gene-effect model, Chronos (Dempster 2021) DepMap-standard population-dynamics + CN model with lowest residual bias, the decision tree by data availability, the Spearman LFC-vs-CN diagnostic, focal-amplification examples (ERBB2 in HER2+, MYC in colorectal, FGFR1 in head and neck), and CRISPRi/a alternatives that bypass the artifact. Use when screening cancer cell lines, diagnosing essentiality at amplified loci, choosing CRISPRcleanR / CERES / Chronos, deciding whether CN correction is needed before MAGeCK / BAGEL2 / drugZ, or switching from Cas9 to CRISPRi.

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Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.

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Call protein-RNA binding sites from CLIP-seq BAM with CLIPper, PureCLIP, Skipper, Piranha, omniCLIP, CTK, CLAM, or Paraclu. Use when choosing between coverage-based, HMM-based, beta-binomial window-based, and crosslink-site-based peak callers; applying ENCODE eCLIP thresholds (log2 IP/SMInput >= 3, -log10 p >= 3); deciding when SMInput is mandatory; or reconciling peak-set discordance between callers for the same RBP.

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Discover RBP binding motifs from CLIP-seq peaks or single-nucleotide crosslink sites using HOMER, MEME/STREME, kpLogo, mCross (CL-position-registered motifs), PEKA (positional k-mer enrichment), RBPamp (affinity), and RNA Bind-n-Seq (RBNS) cross-validation. Use when characterizing RBP sequence specificity, registering motifs to crosslink positions, validating in vivo CLIP motifs against in vitro RBNS Kd, reconciling motif disagreements across tools, or correcting for the uracil crosslinking bias that contaminates raw CLIP motif logos.

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Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF calibration (BF >6 ≈ 90% posterior per Hart 2017; ~5% FDR by BAGEL convention). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.

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Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.

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Preprocess CLIP-seq reads (eCLIP, iCLIP, iCLIP2, iCLIP3, irCLIP, PAR-CLIP, FLASH) with protocol-specific UMI extraction, adapter trimming, length filtering, and post-alignment PCR-duplicate collapse. Use when raw CLIP FASTQ must be turned into deduplicated, crosslink-preserving BAM input for peak calling; choosing between two-pass and single-pass adapter trimming; deciding minimum read length; or mapping UMI patterns to specific eCLIP/iCLIP/iCLIP2/iCLIP3 library preps.

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Comprehensive quality control for CLIP-seq libraries (eCLIP, iCLIP, iCLIP2, PAR-CLIP) covering library complexity (preseq), FRiP, IDR replicate reproducibility, read-distribution metagene, SMInput vs IgG control rationale, rRNA / snoRNA contamination, fragment-length distribution, and ENCODE-compliance thresholds. Use when assessing whether a CLIP library passed, deciding lenient vs stringent peak thresholds, comparing replicates with IDR rescue and self-consistency ratios, or distinguishing failed IP from over-amplified library.

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Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.

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Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.

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Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinical-databases/acmg-classification.

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Analyze codon usage and calculate CAI (Codon Adaptation Index), RSCU, and Nc with Biopython, and produce naive max-CAI codon-optimized sequences. Use when scoring a gene's codon bias against a host, optimizing a CDS for heterologous expression, or studying synonymous codon selection.

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Resolve subclonal copy number, whole-genome doubling, and copy-number tumor evolution from bulk sequencing with Battenberg, TITAN, and MEDICC2. Covers clonal versus subclonal copy-number states, haplotype phasing for subclonal resolution, cancer cell fraction, whole-genome-doubling detection and timing relative to mutations, mirrored subclonal allelic imbalance, and copy-number phylogenies. Use when a tumor is heterogeneous and bulk data shows non-integer copy number, when calling subclonal CNAs, detecting or timing whole-genome doubling, reconstructing copy-number evolution, or deciding between Battenberg and TITAN.

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Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Colic et al. 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving greater sensitivity to small-effect hits than MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).

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Designs covalent inhibitors and warheads targeting cysteine, lysine, serine, threonine, tyrosine, and aspartate residues, with explicit handling of warhead reactivity (acrylamide, chloroacetamide, vinyl sulfone, sulfonyl fluoride, fluorosulfate, aldehyde, boronate, nitrile), reversibility (kinact/Ki, t_residence), glutathione (GSH) stability, intrinsic reactivity assays, and covalent docking (DOCKovalent, GOLD, HCovDock). Use when designing covalent inhibitors for targeted covalent inhibition (TCI), KRAS G12C-style approaches, or rationalizing covalent SAR.

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Analyzes base-editing screens for variant function. Covers library design (Hanna 2021 ClinVar-scale CBE screen benchmarked on BRCA1/2, Cuella-Martin 2021 DDR saturation), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end; 4-7 for ABE7.10), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.

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Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.

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Resolve the architecture of focal oncogene amplifications — extrachromosomal DNA (ecDNA), breakage-fusion-bridge (BFB) cycles, homogeneously staining regions (HSR), and linear amplification — from whole-genome sequencing with AmpliconArchitect, the AmpliconSuite pipeline, and AmpliconClassifier. Covers copy-number seed selection, breakpoint-graph reconstruction, balanced-flow optimization, ecDNA classification, and the limits of depth-only amplification calls. Use when a focal amplification needs structural characterization, when distinguishing ecDNA from chromosomal amplification, suspecting ecDNA-driven oncogene amplification or therapy resistance, or selecting copy-number seeds for amplicon reconstruction.

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Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Nat Commun 12:1302), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.

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Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.

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Classify constitutional (germline) copy number variants for clinical reporting using the 2019 ACMG/ClinGen technical standards points-based framework, with ClassifyCNV and AnnotSV for semi-automated scoring. Covers the separate copy-number-loss and copy-number-gain rubrics, the five-tier classification, ClinGen haploinsufficiency/triplosensitivity and dosage-sensitive regions, de novo and segregation evidence, and population-frequency benign evidence. Use when assigning pathogenic/likely-pathogenic/VUS/likely-benign/benign to a constitutional CNV, scoring a CNV against ACMG/ClinGen criteria, or distinguishing the automatable evidence from the case-specific evidence requiring manual input.

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Call copy number variants with the GATK best-practices workflows — the somatic CNV pipeline (CollectReadCounts, DenoiseReadCounts with tangent normalization, ModelSegments, CallCopyRatioSegments) and the germline GATK-gCNV pipeline (DetermineGermlineContigPloidy, GermlineCNVCaller cohort/case mode, PostprocessGermlineCNVCalls). Covers panel-of-normals construction, AnnotateIntervals/FilterIntervals, allelic-count integration, and QS-based filtering. Use when integrating CNV calling into a GATK variant pipeline, calling rare germline CNVs from an exome cohort, deciding between the somatic and germline GATK workflows, or diagnosing why tangent normalization removed a real event or why gCNV output has low precision.

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