Integrative Deep Models for Alternative Splicing

Anupama Jha; Matthew R. Gazzara; Yoseph Barash

doi:10.1101/104869

CARVIEW

MOTORHOMES

Select Language

HTTP/2 301 date: Mon, 19 Jan 2026 02:55:45 GMT content-length: 0 location: https://doi.org/10.1101/104869 server: cloudflare vary: Origin expires: Tue, 20 Jan 2026 02:55:45 GMT permissions-policy: interest-cohort=(),browsing-topics=() cf-cache-status: DYNAMIC nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} strict-transport-security: max-age=31536000; includeSubDomains; preload report-to: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=14mgiy%2F1K5Thzv%2Fb1A0LlVYsLnJqsZv8EDxSZ%2FF81Dj%2BfKOEDJtr3mDimq9TNWpczWdWJ70eyOd7gFEffOdqCQUzEmVmKg%3D%3D"}]} cf-ray: 9c030e14cc2020c5-BLR alt-svc: h3=":443"; ma=86400 HTTP/2 302 date: Mon, 19 Jan 2026 02:55:45 GMT content-type: text/html;charset=utf-8 location: https://biorxiv.org/lookup/doi/10.1101/104869 server: cloudflare vary: Origin vary: Accept expires: Mon, 19 Jan 2026 03:07:49 GMT permissions-policy: interest-cohort=(),browsing-topics=() cf-cache-status: DYNAMIC nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} strict-transport-security: max-age=31536000; includeSubDomains; preload report-to: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=crKEdj6rO%2B2jkKh2zH7%2F8MCHb7IGfHzlpJxPh4lAupfGaNzpm2pFnKtoAiw2xqk6eLOfLZFE%2FPAABFT%2Fw9QYCNzaA%2FIYGQ%3D%3D"}]} cf-ray: 9c030e151c6420c5-BLR alt-svc: h3=":443"; ma=86400 HTTP/1.1 302 Found Date: Mon, 19 Jan 2026 02:55:46 GMT Content-Type: text/html; charset=iso-8859-1 Transfer-Encoding: chunked Connection: keep-alive server: cloudflare location: https://www.biorxiv.org/lookup/doi/10.1101/104869 cf-cache-status: DYNAMIC Nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} Report-To: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=LmODi%2BZmMF0uOLZ1CyyeyPJD9JmXk1k2qqpV6kX2kJBcRUGSrj%2FVN3XkUBz6ZyLEJ84qzE%2FIKmzsjcBy0Cj9CReLS6YtNhKdUcyk"}]} CF-RAY: 9c030e15af63bc9a-BOM alt-svc: h3=":443"; ma=86400 HTTP/2 301 date: Mon, 19 Jan 2026 02:55:46 GMT content-type: text/html; charset=UTF-8 location: https://www.biorxiv.org/content/10.1101/104869v1 cf-ray: 9c030e18e9d0c1c2-BLR x-content-type-options: nosniff x-content-type-options: nosniff x-drupal-cache: MISS expires: Mon, 19 Jan 2026 03:25:46 GMT cache-control: public, max-age=1800 x-varnish-cache: pragma: no-cache vary: Accept-Encoding x-highwire-sitecode: biorxiv x-highwire-smart-code: biorxiv_production x-varnish: 699221056 x-varnish-ttl: via: 1.1 varnish cf-cache-status: EXPIRED set-cookie: __cf_bm=4QgOrzdBl08BMSKgakEiaVB9U.Gp3jz086t1yoKWBo8-1768791346-1.0.1.1-jKRWitRdfVpVNdw1wivCn.cM3BoPTGFFOfw4tthIZrE_sK_pgQ0oi1W.Wsecg8L7szydXCNSN7Rj25k5YdPf_AphtXbCVN3etvn81dwMu7o; path=/; expires=Mon, 19-Jan-26 03:25:46 GMT; domain=.www.biorxiv.org; HttpOnly; Secure; SameSite=None server: cloudflare HTTP/2 200 date: Mon, 19 Jan 2026 02:55:48 GMT content-type: text/html; charset=utf-8 content-encoding: gzip x-content-type-options: nosniff x-content-type-options: nosniff x-drupal-cache: MISS expires: Sun, 19 Nov 1978 05:00:00 GMT cache-control: no-cache, must-revalidate set-cookie: SSESS1dd6867f1a1b90340f573dcdef3076bc=lSnD1Nq3xFqpgZ8nJXMm1okiBINZGqZCFdKU_WIzjm4; expires=Wed, 11-Feb-2026 06:29:07 GMT; path=/; domain=.biorxiv.org; secure; HttpOnly content-language: en x-frame-options: SAMEORIGIN x-generator: Drupal 7 (https://drupal.org) link: ; rel="canonical",; rel="shortlink" vary: Accept-Encoding x-highwire-sitecode: biorxiv x-highwire-smart-code: biorxiv_production x-varnish: 1897097764 age: 0 via: 1.1 varnish x-varnish-ttl: x-varnish-cache: cf-cache-status: DYNAMIC server: cloudflare cf-ray: 9c030e1d2b96c1c2-BLR Integrative Deep Models for Alternative Splicing | bioRxiv

Abstract

Advancements in sequencing technologies have highlighted the role of alternative splicing (AS) in increasing transcriptome complexity. This role of AS, combined with the relation of aberrant splicing to malignant states, motivated two streams of research, experimental and computational. The First involves a myriad of techniques such as RNA-Seq and CLIP-Seq to identify splicing regulators and their putative targets. The second involves probabilistic models, also known as splicing codes, which infer regulatory mechanisms and predict splicing outcome directly from genomic sequence. To date, these models have utilized only expression data. In this work we address two related challenges: Can we improve on previous models for AS outcome prediction and can we integrate additional sources of data to improve predictions for AS regulatory factors. We perform a detailed comparison of two previous modeling approaches, Bayesian and Deep Neural networks, dissecting the confounding effects of datasets and target functions. We then develop a new target function for AS prediction and show that it significantly improves model accuracy. Next, we develop a modeling framework to incorporate CLIP-Seq, knockdown and over-expression experiments, which are inherently noisy and suffer from missing values. Using several datasets involving key splice factors in mouse brain, muscle and heart we demonstrate both the prediction improvements and biological insights offered by our new models. Overall, the framework we propose offers a scalable integrative solution to improve splicing code modeling as vast amounts of relevant genomic data become available.

Availability: code and data will be available on Github following publication.

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.