Filippo Aluffi-Pentini

Bio: Filippo Aluffi-Pentini is an academic researcher from Sapienza University of Rome. The author has contributed to research in topics: Tandem repeat & Differential equation. The author has an hindex of 8, co-authored 21 publications receiving 356 citations.

Hidden Markov Models in Bioinformatics

Abstract: Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantages and drawbacks. We then consider the major bioinformatics applications, such as alignment, labeling, and profiling of sequences, protein structure prediction, and pattern recognition. We finally provide a critical appraisal of the use and perspectives of HMMs in bioinformatics.

STRING: finding tandem repeats in DNA sequences

Abstract: Motivation and results: The importance of Tandem Repeats in some genomes is now well established. We have reported elsewhere some interesting new results obtained by means of a preliminary program for finding Tandem Repeats in DNA sequences, together with a brief description of the basic ideas of the algorithm. We describe here a completely new program based only in part on those ideas, we briefly discuss the interpretation of the results, and, by way of example, we provide a few novel results relative to the parasites responsible of two re-emerging diseases, Plasmodium falciparum and Mycobacterium tuberculosis. Our program is portable, effective, powerful and fast: it can run on current desktop computers, and it finds all significant Tandem Repeats also in the longest segments of sequences in databases (up to millions of bases), in short times (minutes). Availability: An academic version of the algorithm (full source listing in standard C language) can be freely downloaded (http://www.caspur.it/∼castri/STRING/). Contact: valerio.parisi@roma2.infn.it Supplementary information: Some illustrative figures and some sample results are provided as supplementary material at: http://www.caspur.it/∼castri/STRING/.

A global optimization algorithm using stochastic differential equations

Abstract: SIGMA is a set of FORTRAN subprograms for solving the global optimization problem, which implements a method founded on the numerical solution of a Cauchy problem for a stochastic differential equation inspired by statistical mechanics.This paper gives a detailed description of the method as implemented in SIGMA and reports the results obtained by SIGMA attacking, on two different computers, a set of 37 test problems which were proposed elsewhere by the present authors to test global optimization software.The main conclusion is that SIGMA performs very well, solving 35 of the problems, including some very hard ones.Unfortunately, the limited results available to us at present do not appear sufficient to enable a conclusive comparison with other global optimization methods.

Algorithm 667: Sigma—a stochastic-integration global minimization algorithm

Abstract: Le progiciel presente est un ensemble de sous-programmes FORTRAN, utilisant une arithmetique virgule flottante a double precision, et ayant pour objectif la determination d'un minimum global d'une fonction reelle f(x)=f(x (1) , …, x (N) ) a N variables reelles

A differential-equations algorithm for nonlinear equations

Abstract: DAFNE is a set of FORTRAN subprograms for solving nonlinear equations that implements a method founded on the numerical solution of a Cauchy problem for a system of ordinary differential equahons inspired by classical mechanics. This paper gives a detailed description of the method as implemented in DAFNE and reports on the numerical tests that have been performed; the DAFNE package is described in the accompanying Algorithm. The main conclusions are that DAFNE improves in different substantial respects upon a previous FORTRAN implementation of the same method, and compares favorably with existing software.

Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review.

Abstract: Microsatellites, or tandem simple sequence repeats (SSR), are abundant across genomes and show high levels of polymorphism. SSR genetic and evolutionary mechanisms remain controversial. Here we attempt to summarize the available data related to SSR distribution in coding and noncoding regions of genomes and SSR functional importance. Numerous lines of evidence demonstrate that SSR genomic distribution is nonrandom. Random expansions or contractions appear to be selected against for at least part of SSR loci, presumably because of their effect on chromatin organization, regulation of gene activity, recombination, DNA replication, cell cycle, mismatch repair system, etc. This review also discusses the role of two putative mutational mechanisms, replication slippage and recombination, and their interaction in SSR variation.

The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics

Abstract: The study of microorganisms that pervade each and every part of this planet has encountered many challenges through time such as the discovery of unknown organisms and the understanding of how they interact with their environment. The aim of this review is to take the reader along the timeline and major milestones that led us to modern metagenomics. This new and thriving area is likely to be an important contributor to solve different problems. The transition from classical microbiology to modern metagenomics studies has required the development of new branches of knowledge and specialization. Here, we will review how the availability of high-throughput sequencing technologies has transformed microbiology and bioinformatics and how to tackle the inherent computational challenges that arise from the DNA sequencing revolution. New computational methods are constantly developed to collect, process, and extract useful biological information from a variety of samples and complex datasets, but metagenomics needs the integration of several of these computational methods. Despite the level of specialization needed in bioinformatics, it is important that life-scientists have a good understanding of it for a correct experimental design, which allows them to reveal the information in a metagenome.