Data-Driven Thyroglobulin Cutoffs for Low- and Intermediate-Risk Thyroid Cancer Follow-Up: ITCO Real-World Analysis.

Context: The utility of thyroglobulin (Tg) in the follow-up of differentiated thyroid cancer (DTC) patients has been well-documented. Although third-generation immunoassays have improved accuracy, limitations persist (interfering anti-Tg antibodies and measurement variability). Evolving treatment strategies require a reevaluation of Tg thresholds for optimal patient management.

Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment.

Some scientific studies involve huge amounts of bioinformatics data that cannot be analyzed on personal computers usually employed by researchers for day-to-day activities but rather necessitate effective computational infrastructures that can work in a distributed way. For this purpose, distributed computing systems have become useful tools to analyze large amounts of bioinformatics data and to generate relevant results on virtual environments, where software can be executed for hours or even days without affecting the personal computer or laptop of a researcher. Even if distributed computing resources have become pivotal in multiple bioinformatics laboratories, often researchers and students use them in the wrong ways, making mistakes that can cause the distributed computers to underperform or that can even generate wrong outcomes.

DIAMIN: a software library for the distributed analysis of large-scale molecular interaction networks.

Background: Huge amounts of molecular interaction data are continuously produced and stored in public databases. Although many bioinformatics tools have been proposed in the literature for their analysis, based on their modeling through different types of biological networks, several problems still remain unsolved when the problem turns on a large scale.

Results: We propose DIAMIN, that is, a high-level software library to facilitate the development of applications for the efficient analysis of large-scale molecular interaction networks.

The power of word-frequency-based alignment-free functions: a comprehensive large-scale experimental analysis.

Motivation: Alignment-free (AF) distance/similarity functions are a key tool for sequence analysis. Experimental studies on real datasets abound and, to some extent, there are also studies regarding their control of false positive rate (Type I error). However, assessment of their power, i.

Minimal Extrathyroidal Extension in Predicting 1-Year Outcomes: A Longitudinal Multicenter Study of Low-to-Intermediate-Risk Papillary Thyroid Carcinoma (ITCO#4).

The role of minimal extrathyroidal extension (mETE) as a risk factor for persistent papillary thyroid carcinoma (PTC) is still debated. The aims of this study were to assess the clinical impact of mETE as a predictor of worse initial treatment response in PTC patients and to verify the impact of radioiodine therapy after surgery in patients with mETE. We reviewed all records in the Italian Thyroid Cancer Observatory database and selected 2237 consecutive patients with PTC who satisfied the inclusion criteria (PTC with no lymph node metastases and at least 1 year of follow-up).

FASTA/Q data compressors for MapReduce-Hadoop genomics: space and time savings made easy.

Background: Storage of genomic data is a major cost for the Life Sciences, effectively addressed via specialized data compression methods. For the same reasons of abundance in data production, the use of Big Data technologies is seen as the future for genomic data storage and processing, with MapReduce-Hadoop as leaders. Somewhat surprisingly, none of the specialized FASTA/Q compressors is available within Hadoop.

Alignment-free Genomic Analysis via a Big Data Spark Platform.

Motivation: Alignment-free distance and similarity functions (AF functions, for short) are a well-established alternative to pairwise and multiple sequence alignments for many genomic, metagenomic and epigenomic tasks. Due to data-intensive applications, the computation of AF functions is a Big Data problem, with the recent literature indicating that the development of fast and scalable algorithms computing AF functions is a high-priority task. Somewhat surprisingly, despite the increasing popularity of Big Data technologies in computational biology, the development of a Big Data platform for those tasks has not been pursued, possibly due to its complexity.

Rank-Similarity Measures for Comparing Gene Prioritizations: A Case Study in Autism.

We discuss the challenge of comparing three gene prioritization methods: network propagation, integer linear programming rank aggregation (RA), and statistical RA. These methods are based on different biological categories and estimate disease-gene association. Previously proposed comparison schemes are based on three measures of performance: receiver operating curve, area under the curve, and median rank ratio.

Analyzing big datasets of genomic sequences: fast and scalable collection of k-mer statistics.

Background: Distributed approaches based on the MapReduce programming paradigm have started to be proposed in the Bioinformatics domain, due to the large amount of data produced by the next-generation sequencing techniques. However, the use of MapReduce and related Big Data technologies and frameworks (e.g.

Informational and linguistic analysis of large genomic sequence collections via efficient Hadoop cluster algorithms.

Motivation: Information theoretic and compositional/linguistic analysis of genomes have a central role in bioinformatics, even more so since the associated methodologies are becoming very valuable also for epigenomic and meta-genomic studies. The kernel of those methods is based on the collection of k-mer statistics, i.e.

FASTdoop: a versatile and efficient library for the input of FASTA and FASTQ files for MapReduce Hadoop bioinformatics applications.

Summary: MapReduce Hadoop bioinformatics applications require the availability of special-purpose routines to manage the input of sequence files. Unfortunately, the Hadoop framework does not provide any built-in support for the most popular sequence file formats like FASTA or BAM. Moreover, the development of these routines is not easy, both because of the diversity of these formats and the need for managing efficiently sequence datasets that may count up to billions of characters.