GPTomics/bio-single-cell-splicing

Version Compatibility

Reference examples tested with: MARVEL 2.0+, BRIE2 0.2.4+, scQuint 0.1+, SpliZ 0.0.1+, Sierra 1.0+, Psix 0.1+, anndata 0.10+, scanpy 1.10+, pandas 2.2+, scipy 1.13+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Single-Cell Splicing Analysis

The fundamental decision is chemistry, not tool. Most droplet 3' scRNA-seq cannot support transcriptome-wide splicing inference because reverse transcription primes from the poly(A) tail and most reads land in the 3' UTR — far from CDS-region splicing events. Plate-based full-length methods and single-cell long-read sequencing are the chemistries that give per-cell isoform structure across the gene body.

The 10X 3' Problem (Quantified)

Three compounding mechanisms make 10X Chromium 3' (v3.1, GEM-X, v4) hostile to splicing:

1. 3' enrichment: median fragment <1 kb from poly(A); >70% of unique reads fall within 3' UTR.
2. Short R2 (~91 nt): each read straddles at most one junction; usually none, because R2 lands in 3' UTR.
3. PCR concatemers and TSO artifacts: pollute junction detection; UMI collapse is gene-level, not isoform-level.

Quantitative estimate: Only a small fraction of cassette exons sit close enough to the polyA site to be sampled by 3' chemistry (empirical estimates from APA/3'-end atlases — see Tian & Manley 2017 Nat Rev Mol Cell Biol for the 3' UTR isoform landscape). Effective junction read yield from 10X 3' is <0.1 per cell per AS event — vs the 5-10 needed for stable per-cell PSI. Most splicing analyses on 10X 3' data report artifacts.

The 5' kit (10X 5' GEX) does not solve this — it shifts capture from 3' UTR to 5' UTR / TSS-proximal regions. Marginal improvement; not a transcriptome-wide solution. Note that V(D)J recovery requires the 10X Chromium Single Cell Immune Profiling kit (with TCR/BCR-specific enrichment), not 5' GEX alone — postdocs designing immune-repertoire experiments must use the dedicated V(D)J kit.

Decision: Does the Chemistry Support Splicing Analysis?

| Chemistry | Splicing analysis viable? | Best alternative if no | |-----------|----------------------------|--------------------------| | 10X 3' (Chromium v3, GEM-X, v4, Flex) | No (transcriptome-wide); maybe near-3'-end events | Sierra for APA | | 10X 5' GEX | Limited; near-5'-end events only | Sierra for alternative TSS; switch to MAS-Iso-seq | | Smart-seq2 | Yes (full transcript) | MARVEL or BRIE2 | | Smart-seq3 / Smart-seq3xpress | Yes + UMI molecule counting | MARVEL or BRIE2 | | FLASH-seq | Yes (faster, cheaper Smart-seq3) | MARVEL or BRIE2 | | VASA-seq | Yes + total RNA (incl. nascent, IR) | MARVEL with IR analysis | | STORM-seq | Yes + total RNA + ribodepletion | MARVEL with IR analysis | | MAS-Iso-seq + 10X 5' (PacBio Kinnex) | Yes — full isoforms per cell | FLAMES, scNanoGPS, IsoQuant, see long-read-splicing | | scISOr-Seq2 (PacBio + 10X) | Yes — full isoforms with cell-typing | FLAMES, IsoQuant | | ONT direct cDNA scRNA | Yes | FLAMES | | ONT direct RNA scRNA | Yes + native modifications | FLAMES |

Tool Selection Matrix

| Tool | Best for | Input | Strengths | Fails when | |------|----------|-------|-----------|------------| | MARVEL | Smart-seq plate-based and (v2+) 10X droplet unified workflow | Plate or droplet BAMs + Seurat | SE/A5SS/A3SS/MXE/RI/AFE/ALE; modality classification; native Seurat integration; v2 droplet support | R-only | | BRIE2 | Plate-based with regulatory feature prior | Plate BAM + GFF3 events | Bayesian variational PSI + ELBO_gain test; principled uncertainty; CLI-driven (brie-count, brie-quant) | TensorFlow dependency; slow at scale | | scQuint | Plate-based annotation-free junction-cluster quantification (validated on Smart-seq2) | STAR junctions across cells | Cluster-level junction usage; latent Dirichlet | Authors recommend AGAINST use on 10X 3'/5' data (3'-bias confounds); plate-based only | | SpliZ | Annotation-free discovery of cell-state-associated splicing | STAR-aligned BAMs | Per-gene Z-score; no event database needed | Annotation-free = power tradeoff | | Psix | Regulated AS along trajectories | PSI matrix + kNN graph | Tests graph smoothness; robust to dropout | Needs cell-state graph upstream | | Sierra | APA in 10X 3' (NOT splicing) | 10X BAM + GTF | Peak-calling 3' ends; DEXSeq DTU on UTR isoforms | APA only; not for cassette exons | | pseudobulk leafcutter / rMATS | Between-cell-type differential splicing | Aggregated BAMs | Bulk-level statistical power | Loses within-cluster heterogeneity | | MAS-Iso-seq + FLAMES | Full-length single-cell isoforms | 10X 5' + PacBio Kinnex | Full isoforms per cell at scale | Cost; complex pipeline |

Decision Tree by Goal

| Goal | Recommended approach | |------|----------------------| | "Will my 10X 3' data support splicing?" | No transcriptome-wide; consider Sierra for APA. Note: scQuint authors recommend against use on 10X data | | Cassette exon analysis in cell types from Smart-seq2 | MARVEL with ComputePSI + AssignModality + CompareValues | | Discover cell-state-associated splicing without an event database | SpliZ | | Test regulated AS along developmental pseudotime | Psix | | Per-cell PSI with uncertainty in low-coverage cells | BRIE2 | | Differential splicing between two well-defined cell types | Pseudobulk leafcutter or rMATS on aggregated BAMs | | APA (alternative polyadenylation, often confused with AS) | Sierra | | Full-length single-cell isoforms at scale | MAS-Iso-seq + FLAMES (long-read) | | Microexons (3-27 nt) | Long-read or aligner with low overhang (uLTRA, deSALT) | | snRNA-seq (nuclei) — IR question | Library captures nuclear RNA enriched for incomplete splicing — interpret IR cautiously |

MARVEL Plate-Based Workflow

Goal: Run a unified workflow from STAR junctions to cell-type-specific splicing calls.

Approach: Build a wide splice-junction count matrix (rows = junctions keyed by coord.intron, columns = cells), assemble per-event feature tables, then construct MARVEL object with named slots (SpliceJunction, SplicePheno, SpliceFeature, IntronCounts, GeneFeature, Exp, GTF). Quantify PSI per event class, classify modality, test differential splicing.

library(MARVEL); library(Seurat); library(data.table)

seurat_obj <- readRDS('cells.rds')

# Build wide SJ matrix: first column 'coord.intron' (e.g. 'chr1:100007082:100022621'),
# subsequent columns are per-cell sample IDs with junction counts as values.
# This is constructed from STAR SJ.out.tab files (one per cell) merged on intron coord.
sj_files <- list.files('star_pass2/', pattern='SJ.out.tab$', full.names=TRUE)
sj_long <- rbindlist(lapply(sj_files, function(f) {
    d <- fread(f, sep='\t', header=FALSE,
               col.names=c('chr','start','end','strand','motif','annot','unique','multi','overhang'))
    d$coord.intron <- paste(d$chr, d$start, d$end, sep=':')
    d$sample <- gsub('_SJ.out.tab$', '', basename(f))
    d[, .(coord.intron, sample, unique)]
}))
sj <- dcast(sj_long, coord.intron ~ sample, value.var='unique', fill=0)

# SpliceFeature is a NAMED LIST keyed by event class
df.feature.list <- list(
    SE   = read.table('events_SE.txt',   header=TRUE, sep='\t'),
    A5SS = read.table('events_A5SS.txt', header=TRUE, sep='\t'),
    A3SS = read.table('events_A3SS.txt', header=TRUE, sep='\t'),
    MXE  = read.table('events_MXE.txt',  header=TRUE, sep='\t'),
    RI   = read.table('events_RI.txt',   header=TRUE, sep='\t')
)

# SplicePheno: per-cell metadata; sample.id column maps to SpliceJunction column names
df.pheno <- [email protected]
df.pheno$sample.id <- rownames(df.pheno)

marvel <- CreateMarvelObject(
    SpliceJunction = sj,
    SplicePheno    = df.pheno,
    SpliceFeature  = df.feature.list,
    GeneFeature    = read.table('gene_features.tsv', header=TRUE, sep='\t'),
    Exp            = read.table('tpm.tsv', header=TRUE, sep='\t', row.names=1),
    GTF            = rtracklayer::import('annotation.gtf')
)

marvel <- ComputePSI(marvel, CoverageThreshold=10, EventType='SE')
marvel <- AssignModality(marvel, EventType='SE')
marvel <- CompareValues(
    marvel,
    cell.group.g1 = neurons, cell.group.g2 = glia,
    method = 'wilcox', n.cells = 25, psi.delta = 0.1
)

For 10X droplet data, MARVEL v2+ provides CreateMarvelObject.10x() and AnnotateSJ.10x() constructors. Verify the exact API via ?CreateMarvelObject.10x in installed MARVEL.

MARVEL classifies events into modalities (Song 2017 Mol Cell): included (PSI~1), excluded (PSI~0), bimodal (mixture at 0/1), middle (peaked ~0.5), multimodal. Bimodality usually reflects mixed cell states or stochastic monoallelic-like bursting. Mid-modality (peaked at 0.5) can be technical (mixed cells in a droplet) — confirm with full-length data.

BRIE2 Bayesian PSI

Goal: Estimate per-cell PSI with informative regulatory-feature prior; test cell-state association via likelihood-ratio testing on covariate effects.

Approach: BRIE2 is a CLI-driven workflow (brie-count for read counting, brie-quant for variational inference + LRT). Prepare a GFF3 of splicing events, count cell-barcoded junction reads, then fit the model with covariate testing.

# 1. Count splicing events per cell
brie-count \
    -a splicing_events.gff3 \
    -S sample_list.tsv \
    -o brie_counts/ \
    -p 16

# 2. Fit BRIE2 with LRT against the cell-type covariate
brie-quant \
    -i brie_counts/brie_count.h5ad \
    -c cell_metadata.tsv \
    -o brie_quant.h5ad \
    --interceptMode gene \
    --LRTindex All \
    --testBase null \
    --MCsize 3 \
    --batchSize 1000000 \
    -p 16

--interceptMode gene fits a gene-specific intercept (recommended); --LRTindex All tests all covariates; --testBase null uses the null model as the LRT reference. Verify exact flag set via brie-quant -h in installed BRIE2.

import scanpy as sc

adata_splice = sc.read_h5ad('brie_quant.h5ad')
# Per-event covariate effects, ELBO values, and LRT statistics live in
# adata_splice.varm and adata_splice.var; column names depend on BRIE2 version.
# Inspect with: print(adata_splice); print(adata_splice.varm.keys())
# Per-event significance is typically derived from LRT delta-ELBO.

BRIE2 (Huang & Sanguinetti 2021 Genome Biol) uses a sequence-derived feature prior (exon length, GC content, splice site strength, motif counts) to regularize PSI estimates in low-coverage cells. The LRT-based covariate test answers "is this event associated with cell state?" without requiring per-cell PSI accuracy. Threshold the delta-ELBO at ~3 (analogous to log-Bayes-factor); confirm against version-specific output keys via the brie-tutorials repo.

SpliZ for Annotation-Free Discovery

Goal: Identify splicing-defined cell populations without an event database.

Approach: Compute per-gene splicing Z-score across cells; test for cell-state association via permutation.

spliz \
    --bams sample1.bam sample2.bam \
    --metadata cell_metadata.tsv \
    --gtf annotation.gtf \
    --output spliz_output/ \
    --threads 8

SpliZ (Olivieri 2022 Nat Methods) is robust to dropout because it pools junction information across the gene; particularly useful for discovering splicing diversity in heterogeneous tumor samples.

Psix for Regulated AS Along Trajectories

Goal: Detect AS that varies coherently with cell state along a developmental trajectory, robust to dropout.

Approach: Score whether observed PSI is smooth on the cell-cell kNN graph from expression-space embedding.

import psix
import scanpy as sc

adata = sc.read_h5ad('cells.h5ad')
sc.pp.neighbors(adata, n_neighbors=30, use_rep='X_pca')

psix_obj = psix.Psix(adata, psi_matrix_path='psi_matrix.tsv')
psix_obj.run_psix()

regulated = psix_obj.psix_results.query('psix_score > 1.5 and pvalue < 0.05')

Psix (Buen Abad Najar 2022 Genome Res 32:1385) is the principled alternative to imputing PSI: do not impute (it obliterates heterogeneity); test for graph smoothness instead.

Sierra for APA (Not Splicing)

Goal: Detect alternative polyadenylation in 10X 3' data — frequently confounded with AS.

Approach: Peak-call read pile-ups at 3' ends, then DEXSeq-style DTU on 3' UTR isoforms.

library(Sierra)

peak_file <- FindPeaks(
    output.file = 'peaks.txt',
    gtf.file = 'annotation.gtf',
    bam.file = 'possorted_genome_bam.bam'
)

counts <- CountPeaks(
    peak.sites.file = 'peaks.txt',
    gtf.file = 'annotation.gtf',
    bamfile = 'possorted_genome_bam.bam',
    whitelist.file = 'barcodes.tsv'
)

apa_results <- DUTest(counts, group1 = ctrl_cells, group2 = trt_cells)

If only 10X 3' data is available, this is often what is actually wanted. Distinct UTRs change miRNA targeting, RBP binding, and stability — biologically meaningful but not splicing.

Pseudobulk for Statistical Power

Goal: Recover bulk-level statistical power for differential splicing between cell types.

Approach: Sum junction counts across cells of the same cluster, then run leafcutter / rMATS on aggregated counts.

import pandas as pd
import numpy as np

def pseudobulk_junctions(junction_counts, cell_metadata, groupby='cell_type'):
    out = {}
    for group, cells in cell_metadata.groupby(groupby).groups.items():
        mask = junction_counts.columns.isin(cells)
        out[group] = junction_counts.loc[:, mask].sum(axis=1)
    return pd.DataFrame(out)

Use pseudobulk for differential splicing between well-defined cell types; use per-cell methods for within-population heterogeneity (graded splicing along pseudotime, bimodal cell-state mixtures).

Single-Cell Long-Read = Future of Single-Cell Splicing

In 2024-2026, full-length single-cell long-read sequencing has become practical and is the recommended chemistry for splicing-focused single-cell experiments:

• MAS-Iso-seq / PacBio Kinnex: concatenated full-length cDNA arrays, ~16x throughput vs plain Iso-Seq, compatible with 10X 5' libraries (Al'Khafaji 2024 Nat Biotech)
• scISOr-Seq2: hybrid 10X + PacBio for cell typing + isoform structure (Joglekar et al, scISOr-Seq2 mouse cortex atlas; consult most recent publication for exact citation)
• ONT direct cDNA + 10X: lower cost, similar information content
• FLAMES: barcode demultiplexing + isoform quantification + SNV calling for ONT scRNA (Tian 2021 Genome Biol 22:310)

For splicing-specific full-length single-cell analysis, see long-read-splicing skill.

Per-Tool Failure Modes

MARVEL: SpliceJunction Matrix Format

Trigger: Building the SpliceJunction matrix from STAR SJ.out.tab incorrectly (e.g. long-format instead of wide).

Mechanism: MARVEL plate-based CreateMarvelObject(SpliceJunction = ...) expects a wide matrix with first column coord.intron (formatted chr:start:end) and subsequent columns being per-cell sample IDs with integer junction counts. Long-format data.frames or missing coord.intron column cause runtime errors.

Symptom: "no coord.intron column found" errors; or empty PSI tables despite junction reads being present.

Fix: Verify wide-matrix structure; ensure SJ.out.tabs are merged on the chr:start:end key with cells as columns. Use data.table::dcast for the long->wide reshape.

BRIE2: TensorFlow Memory

Trigger: Large cohort (>10k cells) with deep coverage.

Mechanism: Variational inference loads full count matrix; TensorFlow allocates GPU memory aggressively.

Symptom: OOM kills; training stalls.

Fix: Reduce --batchSize from default (500000) to 100000 or 50000; train per-chromosome batch; use CPU mode for very small cohorts. Note flag is camelCase --batchSize, not --batch_size.

scQuint: 3' Data Sparsity

Trigger: Running scQuint on 10X 3' v3 data hoping for splicing signal.

Mechanism: scQuint's latent Dirichlet model needs junction counts; 10X 3' yields too few junction reads to fit the model robustly.

Symptom: All cells assign to one cluster; no informative splicing signal.

Fix: Pivot to APA analysis with Sierra; or upgrade chemistry to MAS-Iso-seq.

Psix: Missing kNN Graph

Trigger: Running Psix without precomputed cell-cell graph.

Mechanism: Psix tests PSI smoothness on a pre-existing cell-cell graph; without one, no smoothness statistic.

Symptom: Empty results or error about missing connectivities.

Fix: Run sc.pp.neighbors(adata) before Psix; ensure connectivities is in adata.obsp.

Sierra: Annotation Gaps

Trigger: GTF missing 3'UTR annotations.

Mechanism: Sierra peak-calls within annotated 3'UTRs; missing annotations mean missed peaks.

Symptom: Few peaks detected; gene-level coverage but no APA calls.

Fix: Use comprehensive GENCODE annotation; or run de-novo peak calling first.

Reconciliation: When Single-Cell Tools Disagree

| Pattern | Likely cause | Action | |---------|--------------|--------| | MARVEL sig, BRIE2 not | Per-cell PSI noise (BRIE2 conservative); MARVEL pseudobulk-like | Trust MARVEL for cell-type comparisons; BRIE2 for within-cluster | | BRIE2 sig, MARVEL not | Cell-state effect smoother than cell-type boundary | Test along trajectory with Psix | | SpliZ sig, MARVEL not | Annotation-free SpliZ catches novel events | Investigate junction structure manually | | Sierra sig, MARVEL not | Sierra is APA, MARVEL is splicing — different biology | Distinguish in interpretation | | Pseudobulk sig, per-cell not | Power issue; effect averaged out per-cell | Report at cluster level, not per-cell |

Quantitative Concepts Unique to Single-Cell

Per-cell PSI vs pseudobulk PSI:

• Per-cell PSI: meaningful only when junction coverage exceeds ~10-20 reads per cell per event (plate-based or long-read).
• Pseudobulk PSI: aggregate, recovers bulk-level statistical power, discards within-cluster heterogeneity.

Modality detection in PSI distributions (Song 2017 Mol Cell): | Modality | PSI distribution | Biology | |----------|------------------|---------| | Included | Peaked at 1 | Constitutive inclusion | | Excluded | Peaked at 0 | Constitutive skipping | | Bimodal | Mixture at 0 and 1 | Mixed cell states or monoallelic-like bursting | | Middle | Peaked ~0.5 | Often technical (well-contamination, doublets, or low-coverage shrinkage to prior); confirm with full-length | | Multimodal | Multiple peaks | Complex regulation; deserves follow-up |

Beta-binomial vs binomial models: with sparse counts, binomial PSI is overdispersed. Beta-binomial models (BRIE2; leafcutter2 as Dirichlet-multinomial cluster-level) handle this. For very sparse droplet data, even beta-binomial fits poorly per cell — collapse to pseudobulk.

Imputation pitfalls: naive imputation (MAGIC, scImpute, ALRA) of expression matrices is not appropriate for PSI: imputing missing junction counts averages over neighboring cells and obliterates the very heterogeneity under study. Psix's approach — testing smoothness of observed PSI on the kNN graph — is the principled alternative.

Cell-Type-Specific Splicing Biology

| System | Event | Regulator | |--------|-------|-----------| | Neural microexons | 3-27 nt exons enriched in brain | SRRM4 (nSR100); SRRM3 in retina (Irimia 2014 Cell) | | Neural differentiation | PTBP1 -> PTBP2 switch | miR-124 represses PTBP1; derepresses neural exons (Boutz 2007 Genes Dev) | | T-cell activation | CD45 RA -> RO | hnRNP-L, ESRP-mediated | | Erythropoiesis | EPB41 exon 16 | Splicing factor switching during maturation | | Cardiac development | TTN N2BA -> N2B | MBNL1/CELF1 antagonism | | EMT | FGFR2 IIIb -> IIIc, ENAH exon 11a | ESRP1/2 loss in mesenchymal state (Warzecha 2009 Mol Cell) | | Activated T cell | CD45 isoform shift | Multiple SR/hnRNP regulators |

Quality Thresholds

| Metric | Recommendation | |--------|----------------| | Cells per event with reads | >=50 (per-cell PSI); >=200 cells per cluster (pseudobulk) | | Junction reads per event per cell | >=5 with coverage; <=1 = unreliable | | PSI variance for cell-type call | <0.1 within cluster, >0.2 between clusters | | Library | full-length plate or long-read for transcriptome-wide; 3' for APA only | | Doublet filtering | Required before splicing analysis (DoubletFinder, Scrublet) | | Cells per cluster (pseudobulk) | >=100 ideal; >=50 minimum | | nuclear vs whole-cell | snRNA-seq enriches IR; treat with caution |

Common Errors

| Error | Cause | Solution | |-------|-------|----------| | MARVEL: ComputePSI returns empty | STAR SJ.out.tab missing strand info | Re-run STAR with --outSJtype Standard | | brie.tl.fit: NaN loss | Insufficient junction reads per cell | Filter cells with min_reads=20; raise threshold | | scQuint: convergence not reached | LDA model fit on too-few junctions | Aggregate by chromosome; or switch chemistry | | Psix: missing connectivities | Neighbors graph not computed | Run sc.pp.neighbors(adata) first | | Sierra: no peaks called | GTF missing 3'UTR annotations | Use comprehensive GENCODE; or de-novo peak-call | | MARVEL: ggplot error | Seurat version mismatch | Match MARVEL and Seurat versions | | FLAMES: barcode rescue failed | Short-read 10X output not in expected directory | Verify cellranger output structure |

Common Pitfalls

• Treating 10X 3' splicing analysis as legitimate — the chemistry doesn't support it. Use Sierra for APA or upgrade to MAS-Iso-seq.
• Imputing PSI matrices — destroys the heterogeneity to be detected. Use Psix or BRIE2 instead.
• Per-cell PSI on droplet data — typically too sparse for stable estimates. Use pseudobulk first, then drill down to per-cell.
• Confusing APA with splicing — Sierra results look like AS but are 3' UTR isoforms. Different machinery, different biology.
• snRNA-seq IR signal misinterpreted as splicing dysregulation — nuclear RNA is enriched for incompletely spliced transcripts; baseline IR is high.
• Trusting per-cell PSI from BRIE2 without ELBO_gain test — BRIE2's per-cell point estimates are noisy; the principled output is the ELBO_gain cell-state-association statistic.
• Microexon analysis with default short-read aligners — anchors >=20 nt miss most microexons; use VAST-TOOLS, MicroExonator, or long-read.
• Skipping doublet filtering before splicing — doublets create artificial PSI mid-modality.

Related Skills

• single-cell/preprocessing - QC and normalization (must run before splicing)
• single-cell/clustering - Cell type annotation prerequisite
• single-cell/doublet-detection - Doublet filtering critical for splicing
• single-cell/data-io - h5ad / Seurat I/O
• splicing-quantification - Bulk RNA-seq comparison context
• long-read-splicing - Full-isoform analysis from MAS-Iso-seq, scISOr-Seq2; future of single-cell splicing

References

• Huang & Sanguinetti 2021 Genome Biol - BRIE2
• Wen et al 2023 Nucleic Acids Research 51:e29 - MARVEL
• Benegas, Fischer & Song 2022 eLife - scQuint (annotation-free single-cell splicing analysis, validated on Smart-seq2)
• Olivieri et al 2022 Nat Methods - SpliZ
• Buen Abad Najar et al 2022 Genome Research 32:1385 - Psix
• Patrick et al 2020 Genome Biol - Sierra
• Song et al 2017 Mol Cell - splicing modality classification
• Picelli et al 2014 Nat Protoc - Smart-seq2
• Hagemann-Jensen et al 2020 Nat Biotech - Smart-seq3
• Hagemann-Jensen et al 2022 Nat Biotech - Smart-seq3xpress
• Hahaut et al 2022 Nat Biotech - FLASH-seq
• Salmen et al 2022 Nat Biotech - VASA-seq
• Johnson et al 2023 Nat Commun - STORM-seq
• Al'Khafaji et al 2024 Nat Biotech - MAS-Iso-seq / Kinnex
• Tian et al 2021 Genome Biology 22:310 - FLAMES
• Joglekar et al - scISOr-Seq2 mouse cortex atlas (consult most recent publication for venue/year)
• Irimia et al 2014 Cell - neural microexons / SRRM4
• Boutz et al 2007 Genes Dev - PTBP1/PTBP2 neural switch
• Tian & Manley 2017 Nat Rev Mol Cell Biol - alternative polyadenylation and 3' UTR isoforms