Unison is an integrated platform for computational biology discovery freely available at http://unison-db.org/. It contains over 13 million protein sequences, precomputed predictions for over 200 million sequences across 23 feature types, auxiliary data like GO terms and structures, and tools for complex queries and mining of sequences with specified features. Some examples of mining include finding sequences with immunoglobulin domains, transmembrane domains, and ITIM domains, or locating SNPs and domains on protein structures. Unison facilitates diverse computational biology tasks through its comprehensive integrated data and ability to easily perform complex queries and analysis.
The Clinical Significance of Transcript Alignment DiscrepanciesReece Hart
Gene transcripts are the lens through which we understand variants that are identified by genome sequencing, reported in scientific literature, and communicated on clinical reports. An accurate, shared representation of transcripts is essential to communicating variants reliably. This talk presents observations of significant discrepancies between sources of transcripts that will lead to discrepancies in the clinical interpretation of variants, and tools that we have released to contend with these complexities.
The Clinical Significance of Transcript Alignment DiscrepanciesReece Hart
Gene transcripts are the lens through which we understand variants that are identified by genome sequencing, reported in scientific literature, and communicated on clinical reports. An accurate, shared representation of transcripts is essential to communicating variants reliably. This talk presents observations of significant discrepancies between sources of transcripts that will lead to discrepancies in the clinical interpretation of variants, and tools that we have released to contend with these complexities.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
AGRF in conjunction with EMBL Australia recently organised a workshop at Monash University Clayton. This workshop was targeted at beginners and biologists who are new to analysing Next-Gen Sequencing data. The workshop also aimed to provide users with a snapshot of bioinformatics and data analysis tips on how to begin to analyse project data. An introduction to RNA-seq data analysis was presented by AGRF Senior Bioinformatician Dr. Sonika Tyagi.
Presented: 1st August 2012
Genome structure prediction a review over soft computing techniqueseSAT Journals
Abstract There are some techniques like spectrometry or crystallography for the determination of DNA, RNA or protein structures. These processes provide very accurate results for the structure estimation. But these conventional techniques are very slow and could be applied over a few special cases only. Soft computing techniques guarantee a near appropriate results in much smaller time and have very large applicability. These techniques are much easier to apply. Different approaches have been used in soft computing including nature inspired computing for estimation of genome structures with a considerable accuracy of results. This paper provides a review over different soft computing techniques been applied along with application method for the determination of genome structure. Keywords—DNA, RNA, proteins, structure, soft computing, techniques.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
AGRF in conjunction with EMBL Australia recently organised a workshop at Monash University Clayton. This workshop was targeted at beginners and biologists who are new to analysing Next-Gen Sequencing data. The workshop also aimed to provide users with a snapshot of bioinformatics and data analysis tips on how to begin to analyse project data. An introduction to RNA-seq data analysis was presented by AGRF Senior Bioinformatician Dr. Sonika Tyagi.
Presented: 1st August 2012
Genome structure prediction a review over soft computing techniqueseSAT Journals
Abstract There are some techniques like spectrometry or crystallography for the determination of DNA, RNA or protein structures. These processes provide very accurate results for the structure estimation. But these conventional techniques are very slow and could be applied over a few special cases only. Soft computing techniques guarantee a near appropriate results in much smaller time and have very large applicability. These techniques are much easier to apply. Different approaches have been used in soft computing including nature inspired computing for estimation of genome structures with a considerable accuracy of results. This paper provides a review over different soft computing techniques been applied along with application method for the determination of genome structure. Keywords—DNA, RNA, proteins, structure, soft computing, techniques.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Elizabeth Buie - Older adults: Are we really designing for our future selves?
Unison: An Integrated Platform for Computational Biology Discovery
1. Unison: An Integrated Platform for
Computational Biology Discovery
Freely accessible and available at http://unison-db.org/ .
Reece Hart, Kiran Mukhyala
Genentech, Inc.
Pacific Symposium on Biocomputing 2009
2. assert(Sequence Analysis != Sequence Mining)
feature types/models HMM, TM, signal, etc.
sequences
Sequence Analysis
i.e., show predictions for a given sequence
Typically involves minutes to hours of computing per sequence.
Typically entails days to months of computing results.
i.e., show sequences that contain specified features.
Feature-Based Mining
Prediction results
non-redundant superset of all sequences
method-specific data such as score, e-
value, p-value, kinase probability, etc.
parameters
execution arguments/options for
every prediction type and result
3. Unison in a Nutshell
Domain,
Structures
Structure & Homology
& Ligands
Predictions
Protein
Sequences and
Annotations
Genomes, Auxiliary
Gene Mapping & Annotations
Structure, GO, RIF, SCOP,
Probes etc.
Sequences and Annotations Auxiliary Data Precomputed predictions
UniProt, IPI, Ensembl, RefSeq, PDB HomoloGene, Gene Domains, homology, structure, TMs,
STRING, PHANTOM, HUGE, ROUGE, Ontology, taxonomy, localization, signals, disorder, etc.
MGC, Derwent, pataa, nr, etc. PDB, HUGO, SCOP, >200M predictions, 23 types,
>13M seqs, >17k species, 69 origins etc. ~6 CPU-years
4. Unison has many applications.
Unison Web Tools Other In-House Tools Ad Hoc Mining
Mining and
analysis
projects
Domain,
Structures
Structure & Homology
& Ligands
Predictions
Protein
Sequences and
Annotations
Genomes, Auxiliary
Gene Mapping & Annotations
Structure, GO, RIF, SCOP,
Probes etc.
Sequences and Annotations Auxiliary Data Precomputed predictions
UniProt, IPI, Ensembl, RefSeq, PDB HomoloGene, Gene Domains, homology, structure, TMs,
STRING, PHANTOM, HUGE, ROUGE, Ontology, taxonomy, localization, signals, disorder, etc.
MGC, Derwent, pataa, nr, etc. PDB, HUGO, SCOP, >200M predictions, 23 types,
>13M seqs, >17k species, 69 origins etc. ~6 CPU-years
6. Unison is a platform for diverse tools.
Matt Brauer
Guy Cavet
Josh Kaminker
Scott Lohr
Kathryn Woods
Jean Yuan
Peng Yue
7. Unison facilitates complex mining.
Mining for TNF ligands
Mining for E3 Ligases
Mining for 4H Cytokines
Mining for ITxM
Mining for deubiquitinases
Analyzing SNP impact on binding interfaces
Jason Hackney
Nandini Krishnamurthy
Li Li
Yun Li
Jinfeng Liu
Shiu-ming Loh
Kiran Mukhyala
8. Mining for ITIMs the old way.
Ig TM ITIM
➢ Collect sequences.
➢ Prune redundant sequences. (How?!)
➢ For each unique sequence, predict
● Immunoglobulin domains.
● Transmembrane domains.
● ITIM domains.
➢ Write a program that filters predictions.
➢ Summarize hits with external data.
➢ Do it again when source data are updated.
9. Mining for ITIMs the Unison way.
Ig TM ITIM
SELECT IG.pseq_id,
IG.start as ig_start,IG.stop as ig_stop,IG.score,IG.eval,
TM.start as tm_start,TM.stop as tm_stop,
ITIM.start as itim_start,ITIM.stop as itim_stop
FROM pahmm_current_pfam_v IG
JOIN pftmhmm_tms_v TM ON IG.pseq_id=TM.pseq_id AND IG.stop<TM.start
JOIN pfregexp_v ITIM ON TM.pseq_id=ITIM.pseq_id AND TM.stop<ITIM.start
WHERE IG.name='ig' AND IG.eval<1e-2
AND ITIM.acc='MOD_TYR_ITIM';
Ig Ig TM Tm ITIM ITIM
pseq_id start stop score eval start stop start stop best_annotation
234 262 316 30 7.40E-06 440 462 518 523 UniProtKB/Swiss-Prot:SIGL5_HUMAN (RecName: Fu
254 158 213 36 1.90E-07 284 306 386 391 UniProtKB/Swiss-Prot:VSIG4_HUMAN (RecName: F
544 157 215 24 6.60E-04 348 370 431 436 UniProtKB/Swiss-Prot:SIGL9_HUMAN (RecName: Fu
797 254 312 40 7.60E-09 1099 1121 1361 1366 UniProtKB/Swiss-Prot:DCC_HUMAN (RecName: Ful
1113 42 102 30 1.20E-05 243 265 300 305 UniProtKB/Swiss-Prot:KI2L2_HUMAN (RecName: Fu
1114 42 102 30 6.50E-06 243 265 330 335 UniProtKB/Swiss-Prot:KI2L1_HUMAN (RecName: Fu
1115 42 102 31 4.20E-06 243 265 301 306 UniProtKB/Swiss-Prot:KI2L3_HUMAN (RecName: Fu
1116 42 97 30 1.10E-05 339 361 396 401 UniProtKB/TrEMBL:Q95368_HUMAN (SubName: Fu
1134 340 388 26 1.40E-04 603 625 688 693 UniProtKB/Swiss-Prot:PECA1_HUMAN (RecName: F
10. “Are you sure about this Stan? It seems odd that a
pointy head and a long beak is what makes them fly.”
J. Workman, Science 245:1399 (1989)
11. Kiran Mukhyala
Fernando Bazan, Matt Brauer, Jason Hackney, Pete Haverty,
Ken Jung, Josh Kaminker, Nandini Krishnamurthy, Li Li, Yun Li,
Shiuh-ming Loh, Jinfeng Liu, Peng Yue, Jianjun Zhang, Yan Zhang
http://unison-db.org/
Open access web site, downloads, documentation, references
unison-db.org:5432
PostgreSQL & odbc/jdbc/sdbc access
12. Unison Contents
patents HUGO
Geneseq:AAP60074 TNFSF9
1991-10-29
SUNTORY
TNFSF10
TNFSF11
homologs
NP_000585.2 NP_036807.1 | RAT
EP205038-A; New tumour...
NP_000585.2 NP_038721.1 | MOUSE
NP_000585.2 XP_858423.1 | CANFA
GO SNPs
Function P84L
transcription A94T
initiation
elongation
aliases
TNFA_HUMAN
Entrez Q1XHZ6
IPI00001671.1
sequences protein features
gene_id >Unison:98
INCY:1109711.FL1p
symbol MSTESMIRDVE...FGIIAL
CCDS4702.1
locus >Unison:23782
gi:25952111
VRSSSRTPSD...FGIIAL 1 | 23 | | SS
108 | 143 | 1.8e-06 | EGF
162 | 184 | | TM
taxonomy alignments
133 | 138 | | ITIM
9606 Homo sapiens
10090 Mus musculus TNFA 1tnfA
10028 Rattus rattus TNFA 1tnfB
aa-to-resid
loci ...
TNFA 5tswF MSTESMIR
DVEFGIIA
1 233 6+:31651498-31653288
TESMIRDV
IIAMDAC
structures
1tnf SCOP
genomes 1a8m all alpha
Hs35
Hs36
probes 2tun
4tsv
all beta
Ig
HGU133P 5tsw TNF-like
RAT
WHG alpha+beta
13. Ex1: Mine for sequences w/conserved features.
patents HUGO
Geneseq:AAP60074 TNFSF9
1991-10-29
SUNTORY
TNFSF10
TNFSF11
homologs
NP_000585.2 NP_036807.1 | RAT
EP205038-A; New tumour...
NP_000585.2 NP_038721.1 | MOUSE
NP_000585.2 XP_858423.1 | CANFA
GO SNPs
Function P84L
transcription A94T
initiation
elongation
aliases
TNFA_HUMAN
Entrez Q1XHZ6
IPI00001671.1
sequences protein features
gene_id >Unison:98
INCY:1109711.FL1p
symbol MSTESMIRDVE...FGIIAL
CCDS4702.1
locus >Unison:23782
gi:25952111
VRSSSRTPSD...FGIIAL 1 | 23 | | SS
108 | 143 | 1.8e-06 | EGF
162 | 184 | | TM
taxonomy alignments
133 | 138 | | ITIM
9606 Homo sapiens
10090 Mus musculus TNFA 1tnfA
10028 Rattus rattus TNFA 1tnfB
aa-to-resid
loci ...
TNFA 5tswF MSTESMIR
DVEFGIIA
1 233 6+:31651498-31653288
TESMIRDV
IIAMDAC
structures
1tnf SCOP
genomes 1a8m all alpha
Hs35
Hs36
probes 2tun
4tsv
all beta
Ig
HGU133P 5tsw TNF-like
RAT
WHG alpha+beta
14. Ex2: Locate SNPs and Domains on Structure
patents HUGO
Geneseq:AAP60074 TNFSF9
1991-10-29
SUNTORY
TNFSF10
TNFSF11
homologs
NP_000585.2 NP_036807.1 | RAT
EP205038-A; New tumour...
NP_000585.2 NP_038721.1 | MOUSE
NP_000585.2 XP_858423.1 | CANFA
GO SNPs
Function P84L
transcription A94T
initiation
elongation
aliases
TNFA_HUMAN
Entrez Q1XHZ6
IPI00001671.1
sequences protein features
gene_id >Unison:98
INCY:1109711.FL1p
symbol MSTESMIRDVE...FGIIAL
CCDS4702.1
locus >Unison:23782
gi:25952111
VRSSSRTPSD...FGIIAL 1 | 23 | | SS
108 | 143 | 1.8e-06 | EGF
162 | 184 | | TM
taxonomy alignments
133 | 138 | | ITIM
9606 Homo sapiens
10090 Mus musculus TNFA 1tnfA
10028 Rattus rattus TNFA 1tnfB
aa-to-resid
loci ...
TNFA 5tswF MSTESMIR
DVEFGIIA
1 233 6+:31651498-31653288
TESMIRDV
IIAMDAC
structures
1tnf SCOP
genomes 1a8m all alpha
Hs35
Hs36
probes 2tun
4tsv
all beta
Ig
HGU133P 5tsw TNF-like
RAT
WHG alpha+beta
15. Unison can also help you...
➢ Answer more sophisticated questions.
● Require orthologs or a specified exon structure.
➢ Annotate hits.
● Annotate with locus, probes, HUGO gene name,
structures, PubMed refs, external links.
● Group splice forms by locus.
➢ Explore alternatives.
● How do parameters influence results?
● Try other prediction algorithms.
➢ Stay current.
● When new data are available, just rerun the query.
➢ Move on.
● The same data are available to other projects and
other people.