The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

LC-MSMS identification of small molecules; X-Rank, a robust library search algorithm

Identification of small molecules is of major importance for many applications. Liquid Chromatography Tandem Mass spectrometry (LC- MSMS) is gaining increasing interest in the field of small molecule identification. LC-MSMS has a broad range of detection, is sensitive and does not need special sample pre-processing. As a major chal- lenge, spectra of the same compound can show great variability across acquisitions. High spectra variability limits the use of LC-MSMS for library search identifications. Dedicated identification tools such as MS Search from NIST show insufficient performances when it comes to cross-platform identification. In this thesis, we present the new library search scoring model X- Rank. X-Rank matches conserved properties of spectra and proposes a robust probability scoring model. Scoring parameters can be opti- mized from a training set. A re-training of X-Rank for a specific data set, was shown to essentially improve the results. The efficiency of X-Rank was compared to existing solutions, using two test-sets from different machine types. Overall X-Rank showed better results in terms of sensitivity and specificity. Especially in the case of cross-platform identification, X-Rank could better discriminate correct from wrong matches. Furthermore, X-Rank could correctly identify and top rank eight chemical compounds in a test mix. Even though these results confirm an important improvement for cross-platform identification, filters before and after the X-Rank scor- ing are still useful. In this perspective, a new approach to confidently use the retention time information is presented. Furthermore, a spec- tra filtering approach is applied, which improves the identification in terms of quality and speed. Finally, using a specific training configuration, X-Rank was adapted for proteomics data. Combined with the peptide identification tool Phenyx, X-Rank helped matching additional peptides. X-Rank was implemented into the small molecule identification platform SmileMS. SmileMS is designed for a routine use in laborato- ries. It is a multi-user platform, which provides a simple identification workflow and intuitive result visualization. Thanks to the generic software architecture and the mutual inte- gration with the open-source project Java Proteomic Library (JPL), the addition of new methods to SmileMS is facilitated. Such methods in- clude quantification, the combined use of several algorithms, GC-MS and exact mass identification

A multi-target screening analysis in human plasma using fast liquid chromatography-hybrid tandem mass spectrometry (Part I)

Evaluate a new LC-MS/MS screening method for drugs and drugs of abuse as an alternative to the existing methods used in clinical toxicology laboratories

X-Rank: a robust algorithm for small molecule identification using tandem mass spectrometry

The diversity of experimental workflows involving LC-MS/MS and the extended range of mass spectrometers tend to produce extremely variable spectra. Variability reduces the accuracy of compound identification produced by commonly available software for a spectral library search. We introduce here a new algorithm that successfully matches MS/MS spectra generated by a range of instruments, acquired under different conditions. Our algorithm called X-Rank first sorts peak intensities of a spectrum and second establishes a correlation between two sorted spectra. X-Rank then computes the probability that a rank from an experimental spectrum matches a rank from a reference library spectrum. In a training step, characteristic parameter values are generated for a given data set. We compared the efficiency of the X-Rank algorithm with the dot-product algorithm implemented by MS Search from the National Institute of Standards and Technology (NIST) on two test sets produced with different instruments. Overall the X-Rank algorithm accurately discriminates correct from wrong matches and detects more correct substances than the MS Search. Furthermore, X-Rank could correctly identify and top rank eight chemical compounds in a commercially available test mix. This confirms the ability of the algorithm to perform both a straight single-platform identification and a cross-platform library search in comparison to other tools. It also opens the possibility for efficient general unknown screening (GUS) against large compound libraries

The structure of a full-length response regulator from mycobacterium tuberculosis in a stabilised 3D domain-swapped, activated state.

The full-length, two-domain response regulator RegX3 from Mycobacterium tuberculosis is a dimer stabilized by three-dimensional domain swapping. Dimerization is known to occur in the OmpR/PhoB subfamily of response regulators upon activation but has previously only been structurally characterized for isolated receiver domains. The RegX3 dimer has a bipartite intermolecular interface, which buries 2357 A(2) per monomer. The two parts of the interface are between the two receiver domains (dimerization interface) and between a composite receiver domain and the effector domain of the second molecule (interdomain interface). The structure provides support for the importance of threonine and tyrosine residues in the signal transduction mechanism. These residues occur in an active-like conformation stabilized by lanthanum ions. In solution, RegX3 exists as both a monomer and a dimer in a concentration-dependent equilibrium. The dimer in solution differs from the active form observed in the crystal, resembling instead the model of the inactive full-length response regulator PhoB

Distal Stent Graft-Induced New Entry After TEVAR or FET: Insights Into a New Disease From EuREC

Background. The study sought to learn about incidence and reasons for distal stent graft-induced new entry (dSINE) after thoracic endovascular aortic repair (TEVAR) or after frozen elephant trunk (FET) implantation, and develop prevention algorithms. Methods. In an analysis of an international multicenter registry (EuREC [European Registry of Endovascular Aortic Repair Complications] registry), we found 69 dSINE patients of 1430 (4.8%) TEVAR patients with type B aortic dissection and 6 dSINE patients of 100 (6%) patients after the FET procedure for aortic dissection with secondary morphological comparison. Results. The underlying aortic pathology was acute type B aortic dissection in 33 (44%) patients, subacute or chronic type B aortic dissection in 34 (45%) patients, acute type A aortic dissection in 3 patients and remaining dissection after type A repair in 3 (8%) patients, and acute type B intramural hematoma in 2 (3%) patients. dSINE occurred in 4.4% of patients in the acute setting and in 4.9% of patients in the subacute or chronic setting after TEVAR. After the FET procedure, dSINE occurred in 5.3% of patients in the acute setting and in 6.5% of patients in the chronic setting. The interval between TEVAR or FET and the diagnosis of dSINE was 489 +/- 681 days. Follow-up after dSINE was 1340 +/- 1151 days, and 4 (5%) patients developed recurrence of dSINE. Morphological analysis between patients after TEVAR with and without dSINE showed a smaller true lumen diameter, a more accentuated oval true lumen morphology, and a higher degree of stent graft oversizing in patients who developed dSINE. Conclusions. dSINE after TEVAR or FET is not rare and occurs with similar incidence after acute and chronic aortic dissection (early and late). Avoiding oversizing in the acute and chronic settings as well as carefully selecting patients for TEVAR in postdissection aneurysmal formation will aid in reducing the incidence of dSINE to a minimum. (C) 2020 by The Society of Thoracic Surgeon

The science behind 25 years of ovarian stimulation for in vitro fertilization

To allow selection of embryos for transfer after in vitro fertilization, ovarian stimulation is usually carried out with exogenous gonadotropins. To compensate for changes induced by stimulation, GnRH analog cotreatment, oral contraceptive pretreatment, late follicular phase human chorionic gonadotropin, and luteal phase progesterone supplementation are usually added. These approaches render ovarian stimulation complex and costly. The stimulation of multiple follicular development disrupts the physiology of follicular development, with consequences for the oocyte, embryo, and endometrium. In recent years, recombinant gonadotropin preparations have become available, and novel stimulation protocols with less detrimental effects have been developed. In this article, the scientific background to current approaches to ovarian stimulation for in vitro fertilization is reviewed. After a brief discussion of the relevant aspect of ovarian physiology, the development, application, and consequences of ovarian stimulation strategies are reviewed in detail