Base by Base

Guo K et al., Cell Genomics - ProtoCloud is a self-explaining deep generative model that embeds single cells around cell-type-specific prototypes to deliver accurate, uncertainty-aware cell type annotation and gene-level explanations from raw UMI counts. Key terms: single-cell, explainable AI, prototypical models, cell type annotation, uncertainty estimation.

Study Highlights:
ProtoCloud achieves accurate and efficient annotation of single-cell data, including improved detection of rare cell types, by organizing embeddings around learned prototypes. A disentangled latent space separates biological identity from batch and nuisance variation, improving robustness and label transfer. Built-in uncertainty quantification based on cell–prototype similarity identifies and enables correction of misannotations. Prototypical relevance propagation backpropagates similarity to highlight genes driving classification for instant gene‑level explainability.

Conclusion:
By combining a decomposed VAE, learnable prototypes, PRP-based gene relevance, and calibrated similarity-based uncertainty, ProtoCloud provides accurate, interpretable, and robust single-cell annotations that detect rare states, correct label errors, and nominate marker genes to support atlas construction and disease studies.

Music:
Enjoy the music based on this article at the end of the episode.

Article title:
ProtoCloud: A prototypical self-explaining model for single-cell analysis

First author:
Guo K

Journal:
Cell Genomics

DOI:
10.1016/j.xgen.2026.101217

Reference:
Guo K. & Ding J. ProtoCloud: A prototypical self-explaining model for single-cell analysis. Cell Genomics 6, 101217 (2026). doi:10.1016/j.xgen.2026.101217

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

Support:
Base by Base is independent and ad-free — no sponsors, no paywall. If an episode was worth your time, chip in and keep the papers audited and the original songs coming:
❤️ Support monthly: https://buy.stripe.com/cNifZhclVebvagk2JDgEg01
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More at basebybase.com

On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/protocloud-prototypical-self-explaining-single-cell

QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-07-23.

QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Audited the transcript segments describing ProtoCloud architecture, training, uncertainty quantification, and key biological validations (PBMC, RGC time course, EoE).
- transcript topics: ProtoCloud architecture and prototypes; Disentangled latent space with z1 and z2; Prototypical relevance propagation (PRP) and HRGs; Robustness to label noise (20% perturbation); PBMC30K annotation corrections (NKG7 example); Time-course retinal ganglion cells after optic nerve crush

QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 7
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0

Metadata Audited:
- article_doi
- article_title
- article_journal
- license

Factual Items Audited:
- ProtoCloud uses six prototypes per cell type by default
- Latent space is partitioned into two components: z1 for cell-type identity and z2 for batch/noise factors
- PRP identifies gene-level relevance and HRGs (e.g., CD79B, LY9)
- Model robustness to 20% label perturbation yields >93% accuracy
- PBMC30K misannotation is corrected; NKG7 expression is 3.248 in reannotated cytotoxic T cells and ~0.932 in CD4+ T cells (with 3.809 in cytotoxic T cells as a corroborating value)
- Time-course RGC ONC dataset shows reannotation of 30–70% of previously unassigned cells and C1 RGC survival ~5.6%

QC result: Pass.

Staklinski SJ et al., Cell Genomics 6, 101193 (2026) - This episode explores BEAM, a Bayesian framework built on BEAST 2 that jointly infers cell-lineage phylogenies and tissue-migration graphs from CRISPR-based lineage-tracing data. The method quantifies uncertainty, improves reconstruction versus parsimony-based approaches, and supports Bayes-factor hypothesis testing of migration models. Applications to simulated data and mouse lung and prostate datasets reveal complex migration patterns and highlight limits imposed by sparse mutational signal. Key terms: Bayesian inference, metastasis, lineage tracing, phylogenetics, BEAM.

Study Highlights:
BEAM jointly samples lineage trees and tissue-migration histories, producing posterior distributions over migration graphs and timing. In simulations BEAM outperforms existing parsimony-based methods across a range of mutation and migration regimes and is robust to missing barcode data. Applied to mouse lung and prostate datasets, BEAM uncovers complex, heterogeneous migration patterns and provides conservative estimates of metastasis-to-metastasis and primary-reseeding events. The framework also implements Bayes-factor tests to assess dataset informativeness and to compare competing migration models.

Conclusion:
BEAM provides a fully Bayesian approach that integrates lineage-tree and migration-graph inference, quantifies uncertainty, and enables formal hypothesis testing; it improves accuracy in many simulated regimes and reveals richer metastatic histories in real datasets, while its utility is constrained by sparse mutational information and current scalability limits.

Music:
Enjoy the music based on this article at the end of the episode.

Article title:
Bayesian inference of tissue-migration histories in metastatic cancer from cell-lineage tracing data

First author:
Staklinski SJ

Journal:
Cell Genomics 6, 101193 (2026)

DOI:
10.1016/j.xgen.2026.101193

Reference:
Staklinski SJ, Scheben A, Brault LM, Hassett R, Serio RN, Xing J, Nowak DG, Siepel A. Bayesian inference of tissue-migration histories in metastatic cancer from cell-lineage tracing data. Cell Genomics. 2026;6:101193. doi:10.1016/j.xgen.2026.101193

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

Support:
Base by Base is independent and ad-free — no sponsors, no paywall. If an episode was worth your time, chip in and keep the papers audited and the original songs coming:
❤️ Support monthly: https://buy.stripe.com/cNifZhclVebvagk2JDgEg01
☕ One-time donation: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00
More at basebybase.com

On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/beam-bayesian-inference-metastasis

QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-07-23.

QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Audited the transcript's substantive claims about BEAM's methodology, benchmarking against parsimony methods, simulated performance, real-data findings (lung and prostate), Bayes-factor testing, and limitations/future directions; compared against the canonical article text.
- transcript topics: BEAM Bayesian joint inference on BEAST 2; Two-step parsimony methods and their limitations; Simulation benchmarks and edgewise performance; Real-data analyses: lung cancer migration histories; Prostate cancer migration histories and data informativeness; Bayesian hypothesis testing and Bayes factors

QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 5
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0

Metadata Audited:
- article_doi
- article_title
- article_journal
- license

Factual Items Audited:
- BEAM stands for Bayesian Evolutionary Analysis of Metastasis and jointly infers lineage trees and tissue-migration graphs.
- BEAM is implemented on BEAST 2 and uses continuous-time Markov chains (CTMCs) for barcode mutation and tissue migration.
- BEAM outperforms existing parsimony-based methods (MACHINA, PathFinder, Metient, MACH2) in simulations across parameter regimes, especially with higher migration and lower mutation
- BEAM applied to lung and prostate cancer mouse datasets reveals migration-history features; liver metastasis often involves a hub (M-hub pattern) and patterns like LL → RL; BEAM pr
- Only a small fraction of CPs (4 of 421, ~1%) in the prostate dataset were mutationally informative; Bayes-factor-based tests assess informativeness and support for metastatic-resee
- BEAM scalability is limited to roughly 300 cells in current form; migrations are modeled as independent events (co-migrations not explicitly modeled in BEAM as implemented).

QC result: Pass.

Cox RM et al., Cell Genomics 6, 101254 - Cox et al. reconstruct a conserved protein interaction network for the last eukaryotic common ancestor using >26,000 mass spectrometry experiments across 31 species and demonstrate how the ancient interactome predicts and explains modern human disease mechanisms. Key terms: LECA, protein interactome, co-fractionation mass spectrometry, ciliopathy, V-ATPase.

Study Highlights:
The authors inferred a core LECA gene set and integrated ∼26,000 mass spectrometry experiments from 31 eukaryotes to reconstruct a conserved interactome of 109,466 pairwise interactions among 3,193 orthogroups. The map recovers known complexes (e.g., ARP2/3, TRAPP, V-ATPase, HOPS/CORVET) and reveals unexpected ancient interactions and lineage-specific losses. Network propagation on this interactome predicted novel gene-disease links validated experimentally: EFHC2 mislocalization linked to ciliopathic renal failure, ATP6V1A implicated in osteopetrosis with corresponding increased bone density in mouse knockouts, and GLG1 disruption impairing IFT and ciliation relevant to SRTD. The dataset supports a complex LECA capable of cell projection machinery and provides a framework for linking deep conservation to medical phenotypes.

Conclusion:
An experimentally reconstructed LECA interactome defines deeply conserved macromolecular assemblies that have persisted for nearly two billion years and can predict modern disease mechanisms, as shown by validated links to ciliopathies, osteopetrosis, and short-rib thoracic dysplasia.

Music:
Enjoy the music based on this article at the end of the episode.

Article title:
A protein interactome for the last eukaryotic common ancestor illuminates the biochemical basis of modern genetic diseases

First author:
Cox RM

Journal:
Cell Genomics 6, 101254

DOI:
10.1016/j.xgen.2026.101254

Reference:
Cox RM, Papoulas O, Shril S, et al. A protein interactome for the last eukaryotic common ancestor illuminates the biochemical basis of modern genetic diseases. Cell Genomics. 2026;6:101254. https://doi.org/10.1016/j.xgen.2026.101254

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

Support:
Base by Base is independent and ad-free — no sponsors, no paywall. If an episode was worth your time, chip in and keep the papers audited and the original songs coming:
❤️ Support monthly: https://buy.stripe.com/cNifZhclVebvagk2JDgEg01
☕ One-time donation: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00
More at basebybase.com

On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/leca-interactome-modern-disease

QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-07-23.

QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Substantively audited transcript sections: LECA concept and health relevance; CFMS data integration across 31 species; conserved vesicle tethering complexes and actin cytoskeleton in LECA; primordial origins of cell projection/phagocytosis; EFHC2 ciliopathy mechanism; ATP6V1A osteopetrosis; GLG1 in ciliogenesis and SRT
- transcript topics: LECA concept and health relevance; CFMS data integration across 31 species; Conserved vesicle tethering complexes (TRAPP, GARP/COG, HOPS); Actin cytoskeleton and ARP2/3 in LECA; Primordial origins of cell projection and phagocytosis; EFHC2 ciliary mechanism in renal disease

QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 8
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0

Metadata Audited:
- article_doi
- article_title
- article_journal
- license

Factual Items Audited:
- LECA age range ∼1.5–1.8 billion years ago
- Approximately 13,571 human genes map to LECA OGs
- >26,000 mass spectrometry experiments across 31 species
- Final LECA interactome: 109,466 PPIs among 3,193 LECA OGs
- 199 to 2,014 protein complexes in the LECA interactome
- Conservation of vesicle tethering complexes (TRAPP, GARP, COG, HOPS) in LECA

QC result: Pass.

Aguirre M et al., Cell Genomics - Aguirre et al. use simulated gene regulatory networks and a linear structural equation model to show how sparsity, modularity, and hub regulators shape the genome-wide distribution of cis- and trans-heritability of gene expression. Their results indicate gene expression is less polygenic but more pleiotropic than previously thought. Key terms: gene regulatory networks, trans-eQTL, cis-heritability, modularity, hub regulators.

Study Highlights:
The authors model local motifs, modular group structure, and global hub regulators to study their effects on cis- and trans-acting genetic variance. They show that sparsity, modularity, and hub-like out-degree distributions together best reproduce the observed distribution of cis-heritability in human whole-blood data. Hub regulators shorten network paths and concentrate trans-acting variance at pleiotropic loci. Local motifs mediate much of the effect of modular groups on trans-heritability.

Conclusion:
Network topology constrains the genetic architecture of gene expression: realistic GRNs are sparse, modular, and hub-rich, leading to fewer regulators per gene but more repeated use of the same regulators across genes. These findings suggest model and inference biases toward sparse, modular, hub-containing networks and support aggregation strategies to improve trans-eQTL discovery.

Music:
Enjoy the music based on this article at the end of the episode.

Article title:
Regulatory network topology and the genetic architecture of gene expression

First author:
Aguirre M

Journal:
Cell Genomics

DOI:
10.1016/j.xgen.2026.101219

Reference:
Aguirre M, Spence JP, Sella G, Pritchard JK. Regulatory network topology and the genetic architecture of gene expression. Cell Genomics. 2026;6:101219. doi:10.1016/j.xgen.2026.101219.

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

Support:
Base by Base is independent and ad-free — no sponsors, no paywall. If an episode was worth your time, chip in and keep the papers audited and the original songs coming:
❤️ Support monthly: https://buy.stripe.com/cNifZhclVebvagk2JDgEg01
☕ One-time donation: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00
More at basebybase.com

On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/grn-topology-genetic-architecture

QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-07-22.

QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Substantively audited sections covering cis- and trans-heritability, the two-part GRN modeling (graph-generating model + SEM), motifs and hubs, modular structure, and limitations of the approach.
- transcript topics: cis- and trans-eQTLs and their effect sizes; twin-study heritability and cis/trans fractions; graph-based GRN generation: planted partition model (PPM) and modularity; linear structural equation modeling (SEM) of gene expression; hub regulators and heavy-tailed out-degree distributions; network motifs: triangle and diamond (bi-parallel) motifs

QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 6
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0

Metadata Audited:
- article_doi
- article_title
- article_journal
- license

Factual Items Audited:
- cis-eQTL median effect size = 0.14; trans-eQTL median effect size = 0.07
- median cis-heritability fraction h2_cis is 0.28
- 2-hop regulators explain 91.2% of heritability for the median gene
- 10,000 synthetic GRNs simulated; 250 closest matches to real data
- four key architectural properties associated with realistic GRNs: sparsity, positivity, modularity, and hubs
- limitations include linear flow assumption, uniform regulator strength, and bulk whole-blood data at steady state

QC result: Pass.

Rodriguez-Algarra F et al., Cell Genomics - This episode examines a large-scale analysis of germline ribosomal DNA (rDNA) variation in ~500,000 UK Biobank genomes that identifies high-confidence rDNA SNVs and indels associating with human complex traits, notably a cluster in the 28S expansion segment ES15L linked to body-size measures. Key terms: ribosomal DNA, rRNA variants, UK Biobank, ES15L expansion segment, complex traits.

Study Highlights:
The authors derived a stringent set of 378 rDNA variants from UK Biobank whole-genome sequencing and tested intragenomic variant frequencies (IGFs) for association with 419 traits. They report 34 associations at global FDR < 0.01 concentrated in the 28S, with a cluster in ES15L associating with height, weight, and related measures. ES15L variant combinations are predicted to alter rRNA secondary structure and are expressed and incorporated into actively translating ribosomes. rDNA copy number and sequence variation influence overlapping traits but act independently.

Conclusion:
Germline sequence variation within human rDNA is a reproducible and trait-relevant source of genetic variation, with species-specific ES15L variants likely altering rRNA structure and ribosome composition and contributing to variation in body-size and other complex traits.

Music:
Enjoy the music based on this article at the end of the episode.

Article title:
Germline sequence variation within the ribosomal DNA is associated with human complex traits

First author:
Rodriguez-Algarra F

Journal:
Cell Genomics

DOI:
10.1016/j.xgen.2026.101213

Reference:
Rodriguez-Algarra F., Whittaker E., Cooper M., et al. Germline sequence variation within the ribosomal DNA is associated with human complex traits. Cell Genomics. 2026;6:101213. https://doi.org/10.1016/j.xgen.2026.101213

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

Support:
Base by Base is independent and ad-free — no sponsors, no paywall. If an episode was worth your time, chip in and keep the papers audited and the original songs coming:
❤️ Support monthly: https://buy.stripe.com/cNifZhclVebvagk2JDgEg01
☕ One-time donation: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00
More at basebybase.com

On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/germline-rdna-variants-human-traits-ep422

QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-07-22.

QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Audited the transcript for core scientific claims about ES15L rDNA variants: their clustering and trait associations, mechanistic interpretation (RNA structure, translation), evolutionary specificity, and independence from copy number, as presented in the article.
- transcript topics: ES15L expansion segment 15L variants in the 28S subunit; Association of ES15L variants with body-size traits (height, weight, birth weight, waist circumference); Evolutionary and primate comparison of ES15L haplotypes; Polysome-seq evidence for ES15L variant expression and incorporation into translating ribosomes; RNA secondary structure modeling of ES15L variants; Independence of ES15L variant effects from total rDNA copy number

QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 7
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0

Metadata Audited:
- article_doi
- article_title
- article_journal
- license

Factual Items Audited:
- 378 rDNA variants identified after stringent twin-based filtering
- 34 variant–trait associations at global FDR < 0.01
- 17 distinct variants drive the trait associations
- All associations localized within the 28S ES15L region
- Variants are expressed and incorporated into actively translating ribosomes (polysome data)
- ES15L variants form haplotypes and show species-specific patterns (human-specific cluster vs other primates)

QC result: Pass.

Schaff DL et al., Cell Genomics - Schaff et al. use multi-treatment clonal tracing combined with single-cell RNA-seq to show that rare, pre-existing transcriptional states in melanoma predict resistance to diverse therapies and that high CD44 marks cells with multi-treatment tolerance. Key terms: melanoma, CD44, clonal tracing, scRNA-seq, drug resistance.

Study Highlights:
Using high-throughput lentiviral barcoding across six distinct treatments, the authors show that rare melanoma clones can develop resistance to multiple unrelated therapies. Clonal resistance is heritable over roughly six doublings and top resistant clones overlap across treatment pairs. High pre-treatment CD44 expression marks cells that are more likely to resist dabrafenib, trametinib, and hypoxia-mimetic stress, and CD44-high cells show elevated lysosomal activity. Pre-treatment transcriptional programs map to divergent resistant end states, revealing multiple paths to resistance.

Conclusion:
Pre-existing, heritable transcriptional cell states—including a CD44-high program with elevated lysosomal activity—can predict and underlie multi-treatment resistance in melanoma, and mapping these states can expose distinct routes to treatment failure.

Music:
Enjoy the music based on this article at the end of the episode.

Article title:
Pre-existing cell states predict resistance to multiple treatments

First author:
Schaff DL

Journal:
Cell Genomics

DOI:
10.1016/j.xgen.2026.101191

Reference:
Schaff DL, White PE, Cote CJ, et al. Pre-existing cell states predict resistance to multiple treatments. Cell Genomics. 2026;6:101191. doi:10.1016/j.xgen.2026.101191

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

Support:
Base by Base is independent and ad-free — no sponsors, no paywall. If an episode was worth your time, chip in and keep the papers audited and the original songs coming:
❤️ Support monthly: https://buy.stripe.com/cNifZhclVebvagk2JDgEg01
☕ One-time donation: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00
More at basebybase.com

On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/pre-existing-cell-states-predict-resistance

QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-07-22.

QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Audited the core narrative on six-treatment clonal resistance, CD44/FN1 markers, lysosomal mechanism, and CNMF/cNMF-based mapping from pre-treatment states to resistance end-states, plus validation and limitations described in the transcript.
- transcript topics: Six-treatment panel design and outcomes in WM989 melanoma cells; Barcoding and clonal tracing methodology; Pre-treatment gene expression states as predictors of resistance; CD44 and FN1 as markers of multi-treatment resistance; lysosomal sequestration mechanism; CNMF/cNMF analysis linking initial states to resistant end states; Differentiated vs mesenchymal initial states and divergent resistance pathways

QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 6
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0

Metadata Audited:
- article_doi
- article_title
- article_journal
- license

Factual Items Audited:
- Six-treatment panel including two targeted inhibitors (dabrafenib, trametinib), two stressors (CoCl2, acidic media), and two chemotherapeutics (cisplatin, doxorubicin)
- Resistance is heritable over approximately six cellular doublings
- 11 extremely rare clones were captured in the top 10% across all six treatments (generalists)
- CD44 and FN1 are upregulated in pre-treatment clones that later resist multiple treatments
- CD44-high cells exhibit elevated lysosomal activity and lysosomal sequestration markers SRGN and VMP1
- Two pre-treatment transcriptional programs map to divergent resistant end states: differentiated clones (KRAS signaling) vs mesenchymal clones (EMT and oxidative phosphorylation)

QC result: Pass.

Real TD et al., Cell Genomics - This episode examines a long-read sequencing study that resolves the complex NOTCH2NL segmental duplications on human chromosome 1, traces independent duplications in apes, documents gene conversion and structural variation across human haplotypes, and maps paralog-specific regulatory elements using Fiber-seq and long-read transcriptomics in brain organoids. Key terms: NOTCH2NL, segmental duplications, gene conversion, chromatin accessibility, brain organoids.

Study Highlights:
Using 82 long-read assemblies from humans and apes, the authors show independent NOTCH2NL duplications among great apes with protein-coding human copies emerging ~2.2–3.7 mya. Analysis of 69 validated human haplotypes defines 11 structural configurations, reveals frequent interlocus gene conversion and a new paralog (NOTCH2tv), and finds NOTCH2NLA present in all haplotypes. Fiber-seq and long-read Iso-Seq in dorsal forebrain organoids identify paralog-specific accessible chromatin elements correlated with differential transcript abundance, with NOTCH2 and NOTCH2NLA harboring the most unique regulatory sites. Functional assays indicate NOTCH2tv and NOTCH2NLR produce unstable proteins, while NOTCH2NLB yields a stable product in HEK293 tests.

Conclusion:
NOTCH2NL loci underwent dynamic duplication, conversion, and regulatory divergence during ape and human evolution; paralog-specific regulatory elements and structural variation likely shaped expression differences while also increasing genomic instability associated with 1q21.1 copy-number disorders. Long-read genomic and epigenomic approaches are essential to resolve these complex regions and their functional implications.

Music:
Enjoy the music based on this article at the end of the episode.

Article title:
Genetic diversity and regulatory features of human-specific NOTCH2NL duplications

First author:
Real TD

Journal:
Cell Genomics

DOI:
10.1016/j.xgen.2026.101194

Reference:
Real TD, Hebbar P, Yoo D, et al. Genetic diversity and regulatory features of human-specific NOTCH2NL duplications. Cell Genomics. 2026;6:101194. doi:10.1016/j.xgen.2026.101194

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

Support:
Base by Base is independent and ad-free — no sponsors, no paywall. If an episode was worth your time, chip in and keep the papers audited and the original songs coming:
❤️ Support monthly: https://buy.stripe.com/cNifZhclVebvagk2JDgEg01
☕ One-time donation: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00
More at basebybase.com

On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/notch2nl-duplications-regulation

QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-07-21.

QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Audited portions of the transcript covering NOTCH2NL background, independent ape duplications and human copies, interlocus gene conversion (IGC) and the NOTCH2tv paralog, paralog-specific regulatory landscapes (Fiber-seq/FiberFold), organoid transcriptomics, and protein stability assays.
- transcript topics: NOTCH2NL background and brain expansion; Long-read haplotypes and independent duplications; Interlocus gene conversion and NOTCH2tv; Paralog-specific regulatory landscapes and chromatin accessibility; Organoid transcriptomics and protein stability of NOTCH2NL paralogs; Evolutionary timing and limitations of methods

QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 6
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0

Metadata Audited:
- article_doi
- article_title
- article_journal
- license

Factual Items Audited:
- Independent NOTCH2NL duplications occurred in great apes with human protein-coding copies arising in the human lineage.
- NOTCH2NLA is fixed across human haplotypes; NOTCH2NLB/NLC show copy-number variation due to gene conversion and deletions.
- A paralog named NOTCH2tv arose via interlocus gene conversion and lacks the 4 bp deletion necessary for protein stability, remaining a pseudogene.
- Paralog-specific regulatory landscapes exist; NOTCH2 and NOTCH2NLA harbor more paralog-fixed regulatory elements and show higher transcript abundance (~3-fold) relative to other pa
- NOTCH2NLB yields a stable protein in HEK293 cells, whereas NOTCH2tv and NOTCH2NLR do not produce a stable protein.

QC result: Pass.

Hawkins AG et al., Cell Genomics - This episode summarizes Hawkins et al.'s presentation of the Single-Cell Pediatric Cancer Atlas (ScPCA) Portal, a publicly available resource that provides uniformly processed sc/snRNA-seq data and standardized metadata for pediatric tumors. The Portal hosts summarized expression data for over 700 samples across 55 pediatric cancer types, downloadable as SingleCellExperiment or AnnData objects and accompanied by QC reports, automated and curated cell-type annotations, and CNV estimates. The team also introduces scpca-nf, an open-source Nextflow workflow using alevin-fry for efficient, reproducible processing and support for additional modalities such as CITE-seq, cell hashing, bulk RNA-seq, and spatial data. The resource aims to accelerate pediatric cancer research by reducing reprocessing time and enabling cross-sample analyses. Key terms: single-cell RNA-seq, pediatric cancer, data portal, scpca-nf, alevin-fry.

Study Highlights:
The ScPCA Portal aggregates uniformly processed sc/snRNA-seq data for over 700 samples spanning 55 pediatric cancer types and provides downloads ready for analysis in R and Python. Data are processed with an open-source Nextflow workflow (scpca-nf) that uses alevin-fry for fast quantification and includes QC, dimensionality reduction, consensus cell-type annotations, and inferCNV estimates. The Portal supports multimodal datasets (CITE-seq, HTO, bulk RNA-seq, spatial) and provides merged project objects without batch correction to facilitate user-driven integration. OpenScPCA curated annotations complement automated labels to better distinguish malignant from normal cells.

Conclusion:
The ScPCA Portal and scpca-nf deliver a modular, openly accessible platform of standardized pediatric tumor single-cell data and tools to speed reproducible discovery and cross-sample comparisons in pediatric cancer research.

Music:
Enjoy the music based on this article at the end of the episode.

Article title:
The Single-Cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors

First author:
Hawkins AG

Journal:
Cell Genomics

DOI:
10.1016/j.xgen.2026.101283

Reference:
Hawkins AG, Shapiro JA, Spielman SJ, et al. The Single-Cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors. Cell Genomics 6, 101283 (2026). https://doi.org/10.1016/j.xgen.2026.101283

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

Support:
Base by Base is independent and ad-free — no sponsors, no paywall. If an episode was worth your time, chip in and keep the papers audited and the original songs coming:
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On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/scpca-data-portal-tools-single-cell-pediatric-cancer

QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-07-20.

QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Audited sections include portal overview and dataset scope, data formats and access, SCPCA-nf workflow and quality-control steps, annotation strategy and consensus labeling, CNV inference for malignancy, multimodal data modalities, batch-merging limitations, democratization of access, and future directions.
- transcript topics: Portal scope and dataset size; Data formats and download availability; scpca-nf workflow and quantification; Quality control: empty droplets and miQC; Cell-type annotation and ontology-aware consensus; Copy-number variation inference for malignancy

QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 7
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0

Metadata Audited:
- article_doi
- article_title
- article_journal
- license

Factual Items Audited:
- Portal provides uniformly processed sc/snRNA-seq data and de-identified metadata from pediatric tumor samples
- Portal contains over 700 samples across 55 cancer types; as of May 2026, article reports 704 samples
- Downloads are available as SingleCellExperiment or AnnData objects with QC reports, consensus annotations, and CNV estimates
- scpca-nf uses alevin-fry for fast, memory-efficient quantification; benchmarking shows lower runtime/memory than Cell Ranger
- Quality control includes empty-droplet filtering (DropletUtils) and miQC-based filtering
- Cell-type annotation uses SingleR, CellAssign, and SCimilarity with ontology-aware consensus via the latest common ancestor in Cell Ontology

QC result: Pass.

Felici B et al., Cell Genomics - A concise review of how post-GWAS methods are being used to move from statistical associations to translational insights by integrating drug-target prioritization, single-cell resolution of regulatory mechanisms, and imaging-derived organ phenotypes. Key terms: GWAS, drug discovery, single-cell, imaging genetics, polygenic scores.

Study Highlights:
This review synthesizes recent advances in translating GWAS findings into therapeutic targets, single-cell resolved mechanisms, and imaging-derived organ phenotypes. It outlines methods including fine-mapping, sc-eQTL mapping, colocalization, imaging GWAS, and Mendelian randomization to link variants to genes, cell types, proteins, and organ function. Case studies show genetics-informed target prioritization and drug repurposing, and the use of imaging IDPs and polygenic scores to refine discovery and prediction. The authors emphasize key challenges such as limited ancestry diversity, small single-cell cohorts, and difficulty inferring causality across biological scales.

Conclusion:
Integrating GWAS with single-cell and imaging data can accelerate target prioritization and translational discovery, but progress depends on larger, diverse cohorts and improved causal, multi-modal frameworks.

Music:
Enjoy the music based on this article at the end of the episode.

Article title:
Translating genome-wide association studies at multiple scales: Drug target prioritization, cellular architectures, and organ imaging

First author:
Felici B

Journal:
Cell Genomics

DOI:
10.1016/j.xgen.2026.101282

Reference:
Felici B, Chen S, Yuan M, Jiang X, Ip S, Rudd JHF, Inouye M. Translating genome-wide association studies at multiple scales: Drug target prioritization, cellular architectures, and organ imaging. Cell Genomics. 2026;6:101282. doi:10.1016/j.xgen.2026.101282

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

Support:
Base by Base is independent and ad-free — no sponsors, no paywall. If an episode was worth your time, chip in and keep the papers audited and the original songs coming:
❤️ Support monthly: https://buy.stripe.com/cNifZhclVebvagk2JDgEg01
☕ One-time donation: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00
More at basebybase.com

On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/translating-gwas-multiple-scales

QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-07-18.

QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Assessed the transcript's coverage of the multi-scale GWAS translation framework, including cellular and imaging scales, concrete gene/target examples, pharmacogenomics, noncoding variants, brain-heart axis, perturb-seq and AI modeling, and limitations; cross-checked against the canonical text for consistency and accur
- transcript topics: Multi-scale genomics framework (molecular, cellular, organ); Single-cell omics and scQTL causal inference; Imaging genetics and imaging-derived phenotypes (IDPs); Genetic target examples: PCSK9, ANGPTL3, TYK2, BCL11A; Non-coding regulatory variants and gene regulation; Brain-heart-eye axis and cross-organ pleiotropy

QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 8
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0

Metadata Audited:
- article_doi
- article_title
- article_journal
- license

Factual Items Audited:
- Three-scale GWAS translation framework: genetics for therapy prioritization, cellular architectures, imaging genetics
- Targets with human genetic evidence are more likely to succeed (2.6x) in clinical development
- Single-cell sequencing and sc-eQTL mapping link variants to cell-type-specific regulation
- Imaging genetics uses imaging-derived phenotypes (IDPs) and UK Biobank data to connect genetics to organ structure/function
- PCSK9 and ANGPTL3 as target examples; PCSK9 inhibitors are FDA-approved
- TYK2 inhibitor approved for psoriasis; BCL11A linked to sickle cell anemia therapies

QC result: Pass.

Garcia-Salinas OI et al., The American Journal of Human Genetics - Garcia-Salinas et al. develop a bioinformatic pipeline to recover early parental postzygotic mutations (PZMs) from standard-depth (~30×) trio WGS and apply it to 12,015 rare-disease trios, producing a catalog of 1,015 high-confidence autosomal parental PZMs and assessing their genomic features and clinical relevance. Key terms: parental mosaicism, postzygotic mutations, trio WGS, mutational spectrum, clinical genetics.

Study Highlights:
The authors screened unfiltered candidate de novo sites across 12,015 trios and identified 1,015 high-confidence early autosomal parental PZMs with a monomodal blood VAF distribution centered around ~5%. PZMs showed no parental age or sex bias and a distinct substitution spectrum relative to germline DNMs, with enrichment for C>A and T>A and depletion of T>C. Mutational-signature analysis attributed most variants to clock-like signatures SBS1 and SBS5 in similar proportions for PZMs and DNMs. The study recovered clinically relevant missed variants, including likely reportable events in WT1 and DYNC1H1, illustrating diagnostic and recurrence-risk implications.

Conclusion:
A lightweight annotation applied to standard clinical trio WGS can recover early parental mosaic variants at scale, revealing distinct spectral and genomic patterns and identifying clinically actionable variants that routine pipelines often miss, though detection is constrained to an intermediate VAF window by standard-depth sequencing.

Music:
Enjoy the music based on this article at the end of the episode.

Article title:
Landscape of parental postzygotic mutations across >11,000 rare disease trios

First author:
Garcia-Salinas OI

Journal:
The American Journal of Human Genetics

DOI:
10.1016/j.ajhg.2026.06.015

Reference:
Garcia-Salinas OI, Andrews KA, Sanghvi R, et al. Landscape of parental postzygotic mutations across >11,000 rare disease trios. The American Journal of Human Genetics. 2026;113:1–9. https://doi.org/10.1016/j.ajhg.2026.06.015

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

Support:
Base by Base is independent and ad-free — no sponsors, no paywall. If an episode was worth your time, chip in and keep the papers audited and the original songs coming:
❤️ Support monthly: https://buy.stripe.com/cNifZhclVebvagk2JDgEg01
☕ One-time donation: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00
More at basebybase.com

On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/hidden-mosaic-parental-pzms-417

QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-07-18.

QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Audited the transcript sections describing (a) the concept of parental postzygotic mutations (PZMs) and their discovery from standard-depth trio WGS, (b) VAF distributions and timing, (c) mutational spectra and clock-like signatures SBS1/SBS5, (d) GC-content distribution, (e) clinical implications including WT1 and DYN
- transcript topics: Definition and origin of parental postzygotic mutations (PZMs) and mosaicism; Limitations of standard diagnostic pipelines and 30× trio WGS; Bioinformatic pipeline to recover PZMs from DNM candidates; Variant allele fraction (VAF) distribution and timing (approx. third cell division); Mutational spectra and clock-like signatures SBS1/SBS5 in PZMs vs DNMs; GC-content associations and genomic distribution

QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 6
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0

Metadata Audited:
- article_doi
- article_title
- article_journal
- license

Factual Items Audited:
- Parental PZMs can be recovered from standard-depth (~30×) trio WGS
- Identified 1,015 high-confidence early autosomal parental PZMs in 12,015 trios
- PZMs show a monomodal VAF distribution centered near 5%
- PZMs exhibit no parental age or sex bias; roughly 52% of events occur in fathers
- PZMs are enriched for C>A and T>A substitutions and depleted for T>C relative to DNMs; SBS1 and SBS5 contribute similarly to PZMs and DNMs
- Some clinically relevant PZMs (e.g., WT1, DYNC1H1) were missed by routine pipelines and misclassified as DNMs, impacting recurrence risk assessment

QC result: Pass.