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Davide Rambaldi

dblp:87/7097 · DBLP profile ↗
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4ranked-venue papers
4as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 4 · 4 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
cell biology
0.212014
flowFit: a Bioconductor package to estimate proliferation in cell-tracking dye studies · Bioinform. 2014
Bioinformatics and computational biology › genomics
genome visualization
0.112009
FancyGene: dynamic visualization of gene structures and protein domain architectures on genomic loci · Bioinform. 2009

Methods — techniques the papers use, named apart from their topics

peak deconvolution · 0.2mathematical modeling · 0.2web-based visualization · 0.1
YearPublicationVenuePosition
2014 flowFit: a Bioconductor package to estimate proliferation in cell-tracking dye studies
abstract
SUMMARY: Herein we introduce flowFit, a Bioconductor package designed to perform quantitative analysis of cell proliferation in tracking dye-based experiments. The software, distributed as an R Bioconductor library, is based on a mathematical model that takes into account the height of each peak, the size and position of the parental population (labeled but not proliferating) and the estimated distance between the brightness of a cell and the brightness of its daughter (in which the dye is assumed to undergo a 2-fold dilution). Although the algorithm does not make any inference on cell types, rates of cell divisions or rates of cell death, it deconvolutes the actual collected data into a set of peaks, whereby each peak corresponds to a subpopulation of cells that have divided N times. We validated flowFit by retrospective analysis of published proliferation-tracking experiments and demonstrated that the algorithm predicts the same percentage of cells/generation either in samples with discernible peaks (in which the peaks are visible in the collected raw data) or in samples with non-discernible peaks (in which the peaks are fused together). To the best of our knowledge, flowFit represents the first open-source algorithm in its category and might be applied to numerous areas of cell biology in which quantitative deconvolution of tracking dye-based experiments is desired, including stem cell research. AVAILABILITY AND IMPLEMENTATION: http://www.bioconductor.org/packages/devel/bioc/html/flowFit.html (Bioconductor software page). http://www.bioconductor.org/packages/2.13/bioc/vignettes/flowFit/inst/doc/HowTo-flowFit.pdf (package vignette). http://rpubs.com/tucano/flowFit (online tutorial).
Davide Rambaldi, Salvatore Pece, Pier Paolo Di Fiore
Bioinform.1
2009 FancyGene: dynamic visualization of gene structures and protein domain architectures on genomic loci
abstract
Abstract Summary: FancyGene is a fast and user-friendly web-based tool for producing images of one or more genes directly on the corresponding genomic locus. Starting from a variety of input formats, FancyGene rebuilds the basic components of a gene (UTRs, intron, exons). Once the initial representation is obtained, the user can superimpose additional features—such as protein domains and/or a variety of biological markers—in specific positions. FancyGene is extremely flexible allowing the user to change the resulting image dynamically, modifying colors and shapes and adding and/or removing objects. The output images are generated either in portable network graphics (PNG) or portable document format (PDF) formats and can be used for scientific presentations as well as for publications. The PDF format preserves editing capabilities, allowing picture modification using any vector graphics editor. Availability: http://bio.ifom-ieo-campus.it/fancygene Contact: [email protected] Supplementary Information: Details, examples and tutorials can be found at http://bio.ifom-ieo-campus.it/fancygene/tutorial.html and http://bio.ifom-ieo-campus.it/fancygene/help.html
Davide Rambaldi, Francesca D. Ciccarelli
Bioinform.1
2007 Splicy: a web-based tool for the prediction of possible alternative splicing events from Affymetrix probeset data
abstract
BACKGROUND: The Affymetrix technology is nowadays a well-established method for the analysis of gene expression profiles in cancer research studies. However, changes in gene expression levels are not the only way to link genes and disease. The existence of gene isoforms specifically linked with cancer or apoptosis is increasingly found in literature. Hence it is of great interest to associate the results of a gene expression study with updated evidences on the transcript structure and its possible variants. RESULTS: We present here a web-based software tool, Splicy, whose primary task is to retrieve data on the mapping of Affymetrix probes to single exons of gene transcripts and displaying graphically this information projected on the gene physical structure. Starting from a list of Affymetrix probesets the program produces a series of graphical displays, each relative to a transcript associated with the gene targeted by a given probe. The information on the transcript-by-transcript and exon-by-exon mapping of probe pairs can be retrieved both graphically and in the form of tab-separated files. The mapping of single probes to NCBI RefSeq or EMBL cDNAs is handled by the ISREC mapping tables used in the CleanEx Expression Reference Database Project. We currently maintain these mappings for most popular human and mouse Affymetrix chips, and Splicy can be queried for matches with human and mouse NCBI RefSeq or EMBL cDNAs. CONCLUSION: Splicy generates probeset annotations and images describing the relation between the single probes and intron/exon structure of the target transcript in all its known variants. We think that Splicy will be useful for giving to the researcher a clearer picture of the possible transcript variants linked with a given gene and an additional view on the interpretation of microarray experiment data. Splicy is publicly available and has been realized in the framework of a bioinformatics grant from the Italian Cancer Research Association.
Davide Rambaldi, Barbara Felice, Viviane Praz, Philipp Bucher, Davide Cittaro, Alessandro Guffanti
BMC Bioinform.1
2005 NemaFootPrinter: a web based software for the identification of conserved non-coding genome sequence regions between C. elegans and C. briggsae
abstract
BACKGROUND: NemaFootPrinter (Nematode Transcription Factor Scan Through Philogenetic Footprinting) is a web-based software for interactive identification of conserved, non-exonic DNA segments in the genomes of C. elegans and C. briggsae. It has been implemented according to the following project specifications:a) Automated identification of orthologous gene pairs. b) Interactive selection of the boundaries of the genes to be compared. c) Pairwise sequence comparison with a range of different methods. d) Identification of putative transcription factor binding sites on conserved, non-exonic DNA segments. RESULTS: Starting from a C. elegans or C. briggsae gene name or identifier, the software identifies the putative ortholog (if any), based on information derived from public nematode genome annotation databases. The investigator can then retrieve the genome DNA sequences of the two orthologous genes; visualize graphically the genes' intron/exon structure and the surrounding DNA regions; select, through an interactive graphical user interface, subsequences of the two gene regions. Using a bioinformatics toolbox (Blast2seq, Dotmatcher, Ssearch and connection to the rVista database) the investigator is able at the end of the procedure to identify and analyze significant sequences similarities, detecting the presence of transcription factor binding sites corresponding to the conserved segments. The software automatically masks exons. DISCUSSION: This software is intended as a practical and intuitive tool for the researchers interested in the identification of non-exonic conserved sequence segments between C. elegans and C. briggsae. These sequences may contain regulatory transcriptional elements since they are conserved between two related, but rapidly evolving genomes. This software also highlights the power of genome annotation databases when they are conceived as an open resource and the possibilities offered by seamless integration of different web services via the http protocol. AVAILABILITY: The program is freely available at http://bio.ifom-firc.it/NTFootPrinter.
Davide Rambaldi, Alessandro Guffanti, Paolo Morandi, Giuseppe Cassata
BMC Bioinform.1