Scientific publications and

Scientific publications and
archives: media, content
and access

Lesk, Ch 3

(Lesk, 2008)



Scientific literature

•
Scientific publications began as interpersonal communications
–

lectures, seminars and discussions
–

oral communication.

•
Formal written article or books
–

scientific literature.

•
Today, journals, presentation at meetings, books, book chapters,
Web material, films, radio, television programs, podcasts.

•
Formal academic publications must pass the test of ‘peer review’
–

quality control.

•
Before the Internet, scientific literature appeared on paper
(journals). Today, journals appear electronically as well as on paper
(some rarely visit a library to read journals).

•
Delocalized literature delivery and computational methods of
information retrieval.


2

Economic factors governing access to
scholarly publications

3

•
Traditional economic model of scientific journals: a scientific
organization or publisher produces and distribute at regular
intervals, a paper
-
bound ‘issue’ of articles.

–
Cost
: editorial office; preparation of manuscripts;
printing/distribution.

–
Support

(income): sales (subscription), page charges to authors,
donation, subsidy, advertisements etc.

•
Recently, changes:

–
More papers are published
–

driving up costs.

–
Larger volume of publication puts libraries under financial pressure.

–
Electronic facilities reduces costs.

–
Electronic distribution extends the potential format of journal articles.

–
User community supports open access.



Open access / traditional and digital
libraries

•
Redefinition of the author/publisher/reader relationship.

–
Retains peer
-
review process.

–
Accepted articles are placed on the Web, with free access.

–
Authors retain copyright (instead of publisher).

–
Cost of publication are transferred from readers to authors.

•
Traditional libraries
–

you know what it is.

•
Digital libraries.

–
Electronic form, on
-
line.

–
Raise economic questions.

–
Large
-
scale digital libraries by scanning?


4

The information explosion / Databases

•
Efficient delivery can be a mixed blessing.

•
Impossible for anyone to read all the literature in a given field.

•
The Web gives a higher dimension
–

no longer linear, new
media, new way of searching, bibliography management,
organizing and sharing the harvest.


•
Databases: contents, ontology, logical structure, format of the
data, routes for retrieval of data, links to other resources.

•
Literature as a database: e.g. Medline (Medical Literature
Analysis and Retrieval System Online)
–

now part of PubMed,
bibliographic database.




5

Databases

•
Database
organization /
design
–

e.g. design of a relational database
of amino acids.

•
Annotation: a typical entry in a molecular biology database might
contain other information (other than say gene sequences).

–
Reference information (citations of publications).

–
Interpretative information.

–
Links to other information.

•
Database quality control (errors?)

–
“Get it right the first time”: database curation and annotation
–

a new
profession.

–
Identify errors
–

external curators /users.

–
Tracking database changes.




6

Databases

•
Database access: a issue to consider.

•
Links (utility of a database): internal links and external links.

•
Database interoperability: questions that require appeal to multiple
database at once?

–
Merge several databases?

–
Methods for intercommunication between databases?

•
Data mining.

–
Knowledge discovery: description/explanation.

–
Successful forecasting / predictive modeling.

–
Statistical techniques.

–
Artificial neutral networks.

–
Support vector machines.




7

Programming languages and tools

•
Traditional programming languages: FORTRAN, C, C++

•
Scripting languages: PERL, PYTHON, RUBY…

•
Program libraries specialized for molecular biology: standard
libraries (numerical analysis and text processing), libraries for
molecular biology (e.g. bioperl.org).

•
Java
–

Java Virtual Machine
–

computing over the Web?

•
Markup languages: implements data structures, XML.




8

Natural language processing

•
Natural language: verbal
-
oral and/or textual forms of human
-
human communication.

•
Natural language processing has been a goal of computing.

•
Difficulty: ambiguity of words and phrases.

•
Identifying keywords and combinations of keywords: e.g. names of
genes and names of diseases.

•
Knowledge extraction: protein
-
protein interactions (automatic text
-
mining software).

•
Text mining:

–
Identification of references to genes and proteins.

–
Identification of interactions.

–
Interaction networks and diseases.

–
Hypothesis generation (unsuspected relationships between genes and
diseases).



9

Archives and information
retrieval

Lesk, Ch 4

(Lesk, 2008)



Database indexing and specification of
search terms

•
An index: set of pointers to information in a database.

•
Information retrieval programs accepts multiple query terms
and keywords.

•
Possible to ask for logical combinations of indexing terms.

•
Many database search engines allow complex logical
expressions.

•
Follow
-
up questions: modify query, cumulative searches, links
between entries in different databases.

•
Analysis and processing of retrieved data: using results
retrieved in one search as input for another one (some
information retrieval systems provide such facilities).




11

Nucleic acid sequence databases

•
Archiving of bioinformatics data was originally carried out by
individual research groups.

•
As requirements grew, projects become very large
-
scale.

•
Primary data collections related to biological macromolecules:

–
Nucleic acid sequences, including whole
-
genome projects.

–
Amino acid sequence of proteins.

–
Protein and nucleic acid structures.

–
Small
-
molecule crystal structures.

–
Protein functions.

–
Expression patterns of genes.

–
Networks: of metabolic pathways, of gene and protein interactions, and of
control cascades.

–
Publications.



12

Nucleic acid sequence databases

•
Triple partnership of the National Center for Biotechnology
Information (USA); the EMBLBank (European Bioinformatics
Institute, UK) and the Data Bank of Japan (National Institute of
Genetics, Japan).

•
Curate, archive and distribute DNA and RNA sequences.

•
Entries have life history:

–
Unannotated
-
> Preliminary
-
> Unreviewed
-
> Standard

•
Sample entry includes: properties of specific regions (e.g.
coding sequences, performs of affect function, interaction
with other molecules, affect replication, etc)




13

Genome databases and genome browsers

•
Genome browsers (full
-
genome sequences): databases
bringing together all molecular information available about a
particular species.

•
E.g. ensembl.org: intended to be the universal information
source for the human and other genomes.





14

Protein sequence databases

•
In 2002, three protein sequence databases, the Protein
Information Resource (PIR) , USA and SWISS
-
PORT, Swiss and
TrEMBL, Europe, formed the UniPort consortium.

•
Share the database but continue to offer separate
information
-
retrieval tools for access.

•
Databases associated with SWISS
-
PORT:

–
ENZYME DB and PROSITE

•
PIR and associated databases:

–
PIRSF: protein family classification system.

–
iProClass: protein knowledge, access to over 90 biological databases.

–
iProLINK: gateway to protein literature.





15

Databases of protein families

•
Evolutionary relationships / homology detection.

•
Two full
-
length protein sequences (>=100 residues) that have
>=25% identical residues in an optional alignment are likely to
be related.

•
Need sequence alignment algorithms.

•
Refer to a group of related proteins as a family.





16

Databases of structures

•
Structure databases archive, annotate and distribute sets of
atomic coordinates.

•
World
-
wide Protein Data Bank (wwPDB.org).

–
Joint effort of the Research Collaboratory for Structural Bioinformatics
(RCSB) and the Protein Data Bank Japan.

–
Contains the structures of proteins.

–
It overlaps several other databases.

•
Several website offer hierarchical classification of all proteins
of known structure

–
SCOPE, CATH, DALI, CE





17

Other databases

•
Classification and assignment of protein function.

–
The Enzyme Commission.

–
The Gene Ontology Consortium protein function classification.

•
Specialized, or ‘boutique’ databases.

•
Expression (mRNA levels) and proteomics databases
(interpretation in terms of protein patterns).

•
Databases of metabolic pathways (flow of molecules and
energy through pathways of chemical reactions).

•
Bibliographic databases.

•
Only a few of the many databases…




18