1,721,059 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
althonos/pyhmmer: 0.1.3
No full text♦️ pyHMMER
Cython bindings and Python interface to HMMER3.
️ Overview
HMMER is a biological sequence analysis tool that uses profile hidden Markov models to search for sequence homologs. HMMER3 is maintained by members of the the Eddy/Rivas Laboratory at Harvard University.
pyhmmer is a Python module, implemented using the Cython language, that provides bindings to HMMER3. It directly interacts with the HMMER internals, which has the following advantages over CLI wrappers (like hmmer-py):
single dependency: If your software or your analysis pipeline is distributed as a Python package, you can add pyhmmer as a dependency to your project, and stop worrying about the HMMER binaries being properly setup on the end-user machine.
no intermediate files: Everything happens in memory, in Python objects you have control on, making it easier to format your inputs to pass to HMMER without needing to write them to a file. Output retrieval is also done in memory, through instances of the pyhmmer.plan7.TopHits class.
no input formatting: The Easel object model is exposed in the pyhmmer.easel module, and you have the possibility to build a Sequence object yourself to pass to the HMMER pipeline. This is useful if your sequences are already loaded in memory, for instance because you obtained them from another Python library (such as Pyrodigal or Biopython).
no output formatting: HMMER3 is notorious for its numerous output files and its fixed-width tabular output, which is hard to parse (even Bio.SearchIO.HmmerIO is struggling on some sequences).
efficient: Using pyhmmer to launch hmmsearch on sequences and HMMs in disk storage is typically not slower than directly using the hmmsearch binary (see the Benchmarks section). pyhmmer.hmmsearch uses a different parallelisation strategy compared to the hmmsearch binary from HMMER, which helps getting the most of multiple CPUs.
This library is still a work-in-progress, and in a very experimental stage, but it should already pack enough features to run simple biological analyses involving hmmsearch.
Installing
pyhmmer can be installed from PyPI, which hosts some pre-built CPython wheels for x86-64 Linux, as well as the code required to compile from source with Cython:
pydoc pyhmmer.easel
$ pydoc pyhmmer.plan7
Example
Use pyhmmer to run hmmsearch, and obtain an iterable over TopHits that can be used for further sorting/querying in Python:
import pyhmmer
with pyhmmer.easel.SequenceFile("938293.PRJEB85.HG003687.faa") as file:
alphabet = file.guess_alphabet()
sequences = [seq.digitize(alphabet) for seq in file]
with pyhmmer.plan7.HMMFile("Pfam.hmm") as hmms:
all_hits = list(pyhmmer.hmmsearch(hmms, sequences_file, cpus=4))
Processing happens in parallel using Python threads, and a TopHits object is yielded for every HMM passed in the input iterable. Note that for optimal performance, you should pass the number of physical cores to the cpus argument of the pyhmmer.hmmsearch function, as HMMER requires too many SIMD registers to benefit from hyperthreading.
Feedback
⚠️ Issue Tracker
Found a bug ? Have an enhancement request ? Head over to the GitHub issue tracker if you need to report or ask something. If you are filing in on a bug, please include as much information as you can about the issue, and try to recreate the same bug in a simple, easily reproducible situation.
️ Contributing
Contributions are more than welcome! See CONTRIBUTING.md for more details.
⚖️ License
This library is provided under the MIT License. The HMMER3 and Easel code is available under the BSD 3-clause license. See vendor/hmmer/LICENSE and vendor/easel/LICENSE for more information.
This project is in no way not affiliated, sponsored, or otherwise endorsed by the original HMMER authors. It was developed by Martin Larralde during his PhD project at the European Molecular Biology Laboratory in the Zeller team.
Changelog
All notable changes to this project will be documented in this file. The format is based on Keep a Changelog and this project adheres to Semantic Versioning.
v0.1.3 - 2021-01-08
Fixed
Compilation issues in OSX-specific Cython code.
v0.1.2 - 2021-01-07
Fixed
Required Cython files not being included in source distribution.
v0.1.1 - 2020-12-02
Fixed
HMMFile calling file.peek without arguments, causing it to crash when passed some types, e.g. gzip.GzipFile.
HMMFile failing to work with PyPy file objects because of a bug with their implementation of readinto.
C/Python file object implementation using strcpy instead of memcpy, causing issues when null bytes were read.
v0.1.0 - 2020-12-01
Fixed
TextSequence uses the sequence argument it's given on instantiation.
Segmentation fault in Sequence.__eq__ caused by implicit type conversion.
Segmentation fault on SequenceFile.read failure.
Missing type annotations for the pyhmmer.easel module.
v0.1.0-a5 - 2020-11-28
Added
Sequence.__len__ magic method so that len(seq) returns the number of letters in seq.
Python file-handle support when opening an pyhmmer.plan7.HMMFile.
Context manager protocol to pyhmmer.easel.SSIWriter.
Type annotations for pyhmmer.easel.SSIWriter.
add_alias to pyhmmer.easel.SSIWriter.
write method to pyhmmer.plan7.OptimizedProfile to write an optimized profile in binary format.
offsets property to interact with the disk offsets of a pyhmmer.plan7.OptimizedProfile instance.
pyhmmer.hmmer.hmmpress emulating the hmmpress binary from HMMER.
M property to pyhmmer.plan7.HMM exposing the number of nodes in the model.
Changed
Bumped vendored Easel to v0.48.
Bumped vendored HMMER to v3.3.2.
pyhmmer.plan7.HMMFile will raise an EOFError when given an empty file.
Renamed length property to L in pyhmmer.plan7.Background.
Fixed
Segmentation fault when close method of pyhmmer.easel.SSIWriter was called more than once.
close method of pyhmmer.easel.SSIWriter not writing the index contents.
v0.1.0-a4 - 2020-11-24
Added
MSA, TextMSA and DigitalMSA classes representing a multiple sequence alignment to pyhmmer.easel.
Methods and protocol to copy a Sequence and a MSA.
pyhmmer.plan7.OptimizedProfile wrapping a platform-specific optimized profile.
SSIReader and SSIWriter classes interacting with sequence/subsequence indices to pyhmmer.easel.
Exception handler using Python exceptions to report Easel errors.
Changed
pyhmmer.hmmsearch returns an iterator of TopHits, with one instance per HMM in the input.
pyhmmer.hmmsearch properly raises errors happenning in the background threads without deadlock.
pyhmmer.plan7.Pipeline recycles memory between Pipeline.search calls.
Fixed
Missing type annotations for the pyhmmer.errors module.
Removed
Unneeded or private methods from pyhmmer.plan7.
v0.1.0-a3 - 2020-11-19
Added
TextSequence and DigitalSequence representing a Sequence in a given mode.
E-value properties to Hit and Domain.
TopHits now stores a reference to the pipeline it was obtained from.
Pipeline.Z and Pipeline.domZ properties.
Experimental pickling support to Alphabet.
Experimental freelist to Sequence class to avoid allocation bottlenecks when iterating on a SequenceFile without recycling sequence buffers.
Changed
Made Sequence an abstract base class.
Additional Pipeline parameters can be passed as keyword arguments to pyhmmer.hmmsearch.
SequenceFile.read can now be configured to skip reading the metadata or the content of a sequence.
Removed
Redundant SequenceFile methods.
Fixed
doctest loader crashing on Python 3.5.
TopHits.threshold segfaulting when being called without prior Tophits.sort call
Unknown format argument to SequenceFile constructor not raising the right error.
v0.1.0-a2 - 2020-11-12
Added
Support for compilation on PowerPC big-endian platforms.
Type annotations and stub files for Cython modules.
Changed
distutils is now used to compile the package, instead of calling autotools and letting HMMER configure itself.
Bitfield.count now allows passing an argument (for compatibility with collections.abc.Sequence).
v0.1.0-a1 - 2020-11-10
Initial alpha release (test deployment to PyPI)
althonos/pyhmmer: 0.1.0
No full text♦️ pyHMMER
Cython bindings and Python interface to HMMER3.
️ Overview
HMMER is a biological sequence analysis tool that uses profile hidden Markov models to search for sequence homologs. HMMER3 is maintained by members of the the Eddy/Rivas Laboratory at Harvard University.
pyhmmer is a Python module, implemented using the Cython language, that provides bindings to HMMER3. It directly interacts with the HMMER internals, which has the following advantages over CLI wrappers (like hmmer-py):
single dependency: If your software or your analysis pipeline is distributed as a Python package, you can add pyhmmer as a dependency to your project, and stop worrying about the HMMER binaries being properly setup on the end-user machine.
no intermediate files: Everything happens in memory, in Python objects you have control on, making it easier to format your inputs to pass to HMMER without needing to write them to a file. Output retrieval is also done in memory, through instances of the pyhmmer.plan7.TopHits class.
no input formatting: The Easel object model is exposed in the pyhmmer.easel module, and you have the possibility to build a Sequence object yourself to pass to the HMMER pipeline. This is useful if your sequences are already loaded in memory, for instance because you obtained them from another Python library (such as Pyrodigal or Biopython).
no output formatting: HMMER3 is notorious for its numerous output files and its fixed-width tabular output, which is hard to parse (even Bio.SearchIO.HmmerIO is struggling on some sequences).
efficient: Using pyhmmer to launch hmmsearch on sequences and HMMs in disk storage is typically not slower than directly using the hmmsearch binary (see the Benchmarks section). pyhmmer.hmmsearch uses a different parallelisation strategy compared to the hmmsearch binary from HMMER, which helps getting the most of multiple CPUs.
This library is still a work-in-progress, and in a very experimental stage, but it should already pack enough features to run simple biological analyses involving hmmsearch.
Installing
pyhmmer can be installed from PyPI, which hosts some pre-built CPython wheels for x86-64 Linux, as well as the code required to compile from source with Cython:
pydoc pyhmmer.easel
$ pydoc pyhmmer.plan7
Example
Use pyhmmer to run hmmsearch, and obtain an iterable over TopHits that can be used for further sorting/querying in Python:
import pyhmmer
with pyhmmer.easel.SequenceFile("938293.PRJEB85.HG003687.faa") as file:
alphabet = file.guess_alphabet()
sequences = [seq.digitize(alphabet) for seq in file]
with pyhmmer.plan7.HMMFile("Pfam.hmm") as hmms:
all_hits = list(pyhmmer.hmmsearch(hmms, sequences_file, cpus=4))
Processing happens in parallel using Python threads, and a TopHits object is yielded for every HMM passed in the input iterable. Note that for optimal performance, you should pass the number of physical cores to the cpus argument of the pyhmmer.hmmsearch function, as HMMER requires too many SIMD registers to benefit from hyperthreading.
Feedback
⚠️ Issue Tracker
Found a bug ? Have an enhancement request ? Head over to the GitHub issue tracker if you need to report or ask something. If you are filing in on a bug, please include as much information as you can about the issue, and try to recreate the same bug in a simple, easily reproducible situation.
️ Contributing
Contributions are more than welcome! See CONTRIBUTING.md for more details.
⚖️ License
This library is provided under the MIT License. The HMMER3 and Easel code is available under the BSD 3-clause license. See vendor/hmmer/LICENSE and vendor/easel/LICENSE for more information.
This project is in no way not affiliated, sponsored, or otherwise endorsed by the original HMMER authors. It was developed by Martin Larralde during his PhD project at the European Molecular Biology Laboratory in the Zeller team.
Changelog
All notable changes to this project will be documented in this file. The format is based on Keep a Changelog and this project adheres to Semantic Versioning.
v0.1.0 - 2020-12-01
Fixed
TextSequence uses the sequence argument it's given on instantiation.
Segmentation fault in Sequence.__eq__ caused by implicit type conversion.
Segmentation fault on SequenceFile.read failure.
Missing type annotations for the pyhmmer.easel module.
v0.1.0-a5 - 2020-11-28
Added
Sequence.__len__ magic method so that len(seq) returns the number of letters in seq.
Python file-handle support when opening an pyhmmer.plan7.HMMFile.
Context manager protocol to pyhmmer.easel.SSIWriter.
Type annotations for pyhmmer.easel.SSIWriter.
add_alias to pyhmmer.easel.SSIWriter.
write method to pyhmmer.plan7.OptimizedProfile to write an optimized profile in binary format.
offsets property to interact with the disk offsets of a pyhmmer.plan7.OptimizedProfile instance.
pyhmmer.hmmer.hmmpress emulating the hmmpress binary from HMMER.
M property to pyhmmer.plan7.HMM exposing the number of nodes in the model.
Changed
Bumped vendored Easel to v0.48.
Bumped vendored HMMER to v3.3.2.
pyhmmer.plan7.HMMFile will raise an EOFError when given an empty file.
Renamed length property to L in pyhmmer.plan7.Background.
Fixed
Segmentation fault when close method of pyhmmer.easel.SSIWriter was called more than once.
close method of pyhmmer.easel.SSIWriter not writing the index contents.
v0.1.0-a4 - 2020-11-24
Added
MSA, TextMSA and DigitalMSA classes representing a multiple sequence alignment to pyhmmer.easel.
Methods and protocol to copy a Sequence and a MSA.
pyhmmer.plan7.OptimizedProfile wrapping a platform-specific optimized profile.
SSIReader and SSIWriter classes interacting with sequence/subsequence indices to pyhmmer.easel.
Exception handler using Python exceptions to report Easel errors.
Changed
pyhmmer.hmmsearch returns an iterator of TopHits, with one instance per HMM in the input.
pyhmmer.hmmsearch properly raises errors happenning in the background threads without deadlock.
pyhmmer.plan7.Pipeline recycles memory between Pipeline.search calls.
Fixed
Missing type annotations for the pyhmmer.errors module.
Removed
Unneeded or private methods from pyhmmer.plan7.
v0.1.0-a3 - 2020-11-19
Added
TextSequence and DigitalSequence representing a Sequence in a given mode.
E-value properties to Hit and Domain.
TopHits now stores a reference to the pipeline it was obtained from.
Pipeline.Z and Pipeline.domZ properties.
Experimental pickling support to Alphabet.
Experimental freelist to Sequence class to avoid allocation bottlenecks when iterating on a SequenceFile without recycling sequence buffers.
Changed
Made Sequence an abstract base class.
Additional Pipeline parameters can be passed as keyword arguments to pyhmmer.hmmsearch.
SequenceFile.read can now be configured to skip reading the metadata or the content of a sequence.
Removed
Redundant SequenceFile methods.
Fixed
doctest loader crashing on Python 3.5.
TopHits.threshold segfaulting when being called without prior Tophits.sort call
Unknown format argument to SequenceFile constructor not raising the right error.
v0.1.0-a2 - 2020-11-12
Added
Support for compilation on PowerPC big-endian platforms.
Type annotations and stub files for Cython modules.
Changed
distutils is now used to compile the package, instead of calling autotools and letting HMMER configure itself.
Bitfield.count now allows passing an argument (for compatibility with collections.abc.Sequence).
v0.1.0-a1 - 2020-11-10
Initial alpha release (test deployment to PyPI)
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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