35 research outputs found
ΠΡΠΎΠ³ΡΠ°ΠΌΠΌΠ° ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½ΠΈΡ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΡΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡ ΠΏΠ΅ΠΏΡΠΈΠ΄ΠΎΠ² Ρ ΡΡΡΡΠΎΠΌ ΠΏΠΎΡΡΡΡΠ°Π½ΡΠ»ΡΡΠΈΠΎΠ½Π½ΡΡ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠΉ
This paper describes the Retention Time Predictor (RTP) program and web service for predicting the retention time of peptides on a chromatographic column in mass spectrometry experiments. Taking into account post-translational modifications of peptides the program represents a modification of the well-known SSRCalc version 3 (Krokhin, Anal. Chem. 2006, 78(22), 7785-7795). The values of retention coefficients for modified amino acid residues and the algorithm for calculating the isoelectric point value were from the pIPredict program (Skvortsov et al., Biomed. Chem. Res. Meth. 2021, 4(4), e00161). Modifications described in the program include (i) Tandem Mass Tag (TMT) and Isobaric Tags for Relative and Absolute Quantification (iTRAQ) labels; (ii) acetylation, formylation, and methylation of the N-terminal residue and/or lysine side chain; (iii) carbamidomethylation of cysteine, asparagine, and glutamic acid residues; (iv) oxidation and double oxidation of methionine and proline residues; (v) phosphorylation of serine, threonine, and tyrosine residues; (vi) C-terminal amidation of lysine and arginine residues; (vii) formation of propionamide with a cysteine residue. Retention coefficient estimation was based on data from 25 mass spectrometry experiments for which identification was performed from the raw data deposited in the ProteomeXchange database. The RTP program and web service are freely available at http://lpcit.ibmc.msk.ru/RTP.Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π° ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ° ΠΈ web-ΡΠ΅ΡΠ²ΠΈΡ Retention Time Predictor (RTP), ΠΏΡΠ΅Π΄Π½Π°Π·Π½Π°ΡΠ΅Π½Π½ΡΠ΅ Π΄Π»Ρ ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½ΠΈΡ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΡΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡ ΠΏΠ΅ΠΏΡΠΈΠ΄ΠΎΠ² Π½Π° Ρ
ΡΠΎΠΌΠ°ΡΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠ»ΠΎΠ½ΠΊΠ΅ Π² ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Ρ
ΠΏΠΎ ΠΌΠ°ΡΡ-ΡΠΏΠ΅ΠΊΡΡΠΎΠΌΠ΅ΡΡΠΈΠΈ ΠΈ ΡΡΠΈΡΡΠ²Π°ΡΡΠ°Ρ ΠΏΠΎΡΡΡΡΠ°Π½ΡΠ»ΡΡΠΈΠΎΠ½Π½ΡΠ΅ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π°ΠΌΠΈΠ½ΠΎΠΊΠΈΡΠ»ΠΎΡΠ½ΡΡ
ΠΎΡΡΠ°ΡΠΊΠΎΠ² (Π°.ΠΎ.). ΠΡΠΎΠ³ΡΠ°ΠΌΠΌΠ° ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ ΡΠΎΠ±ΠΎΠΉ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΠΈΠ·Π²Π΅ΡΡΠ½ΠΎΠΉ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ SSRCalc Π²Π΅ΡΡΠΈΠΈ 3 (Krokhin, Anal. Chem., 2006, 78(22), 7785β7795). Π Π½Π΅Π΅ Π΄ΠΎΠ±Π°Π²Π»Π΅Π½Ρ Π·Π½Π°ΡΠ΅Π½ΠΈΡ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠΎΠ² ΡΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡ Π΄Π»Ρ ΠΌΠΎΠ΄ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π°.ΠΎ. ΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΡΠ°ΡΡΡΡΠ° Π²Π΅Π»ΠΈΡΠΈΠ½Ρ ΠΈΠ·ΠΎΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΎΡΠΊΠΈ ΠΈΠ· ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ pIPredict (Skvortsov et al., Biomed. Chem. Res. Meth., 2021, 4(4), e00161). ΠΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ, ΠΎΠΏΠΈΡΠ°Π½Π½ΡΠ΅ Π² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ΅, Π²ΠΊΠ»ΡΡΠ°ΡΡ: (i) Tandem Mass Tag (TMT) ΠΈ Isobaric Tags for Relative and Absolute Quantification (iTRAQ) ΠΌΠ΅ΡΠΊΠΈ; (ii) Π°ΡΠ΅ΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅, ΡΠΎΡΠΌΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈ ΠΌΠ΅ΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ N-ΠΊΠΎΠ½ΡΠ΅Π²ΠΎΠ³ΠΎ ΠΎΡΡΠ°ΡΠΊΠ° ΠΈ/ΠΈΠ»ΠΈ Π±ΠΎΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΠ°Π΄ΠΈΠΊΠ°Π»Π° Π»ΠΈΠ·ΠΈΠ½Π°; (iii) ΠΊΠ°ΡΠ±Π°ΠΌΠΈΠ΄ΠΎΠΌΠ΅ΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΎΡΡΠ°ΡΠΊΠΎΠ² ΡΠΈΡΡΠ΅ΠΈΠ½Π°, Π°ΡΠΏΠ°ΡΠ°Π³ΠΈΠ½ΠΎΠ²ΠΎΠΉ ΠΈ Π³Π»ΡΡΠ°ΠΌΠΈΠ½ΠΎΠ²ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡ; (iv) ΠΎΠΊΠΈΡΠ»Π΅Π½ΠΈΠ΅ ΠΈ Π΄Π²ΠΎΠΉΠ½ΠΎΠ΅ ΠΎΠΊΠΈΡΠ»Π΅Π½ΠΈΠ΅ ΠΎΡΡΠ°ΡΠΊΠΎΠ² ΠΌΠ΅ΡΠΈΠΎΠ½ΠΈΠ½Π° ΠΈ ΠΏΡΠΎΠ»ΠΈΠ½Π°; (v) ΡΠΎΡΡΠΎΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΎΡΡΠ°ΡΠΊΠΎΠ² ΡΠ΅ΡΠΈΠ½Π°, ΡΡΠ΅ΠΎΠ½ΠΈΠ½Π° ΠΈ ΡΠΈΡΠΎΠ·ΠΈΠ½Π°; (vi) Π‘-ΠΊΠΎΠ½ΡΠ΅Π²ΠΎΠ΅ Π°ΠΌΠΈΠ΄ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΎΡΡΠ°ΡΠΊΠΎΠ² Π»ΠΈΠ·ΠΈΠ½Π° ΠΈ Π°ΡΠ³ΠΈΠ½ΠΈΠ½Π°; (vii) ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΠΏΠΈΠΎΠ½Π°ΠΌΠΈΠ΄Π° Ρ ΠΎΡΡΠ°ΡΠΊΠΎΠΌ ΡΠΈΡΡΠ΅ΠΈΠ½Π°. ΠΠΎΠ΄Π±ΠΎΡ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠΎΠ² ΡΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡ ΠΏΡΠΎΠ²Π΅Π΄ΡΠ½ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π΄Π°Π½Π½ΡΡ
25 ΠΌΠ°ΡΡ-ΡΠΏΠ΅ΠΊΡΡΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠ², Π΄Π»Ρ ΠΊΠΎΡΠΎΡΡΡ
ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ Π±ΡΠ»Π° Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π° Π·Π°Π½ΠΎΠ²ΠΎ ΠΏΠΎ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ (RAW) Π΄Π°Π½Π½ΡΠΌ, Π΄Π΅ΠΏΠΎΠ½ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌ Π² ΠΠ ProteomeXchange. ΠΡΠΎΠ³ΡΠ°ΠΌΠΌΠ° RTP ΠΈ web-ΡΠ΅ΡΠ²ΠΈΡ ΡΠ²ΠΎΠ±ΠΎΠ΄Π½ΠΎ Π΄ΠΎΡΡΡΠΏΠ½Ρ ΠΏΠΎ Π°Π΄ΡΠ΅ΡΡ http://lpcit.ibmc.msk.ru/RTP
ΠΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½ΠΈΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΡ ΠΈΠ·ΠΎΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΎΡΠΊΠΈ ΠΏΠ΅ΠΏΡΠΈΠ΄ΠΎΠ² ΠΈ Π±Π΅Π»ΠΊΠΎΠ² Ρ ΡΠΈΡΠΎΠΊΠΈΠΌ ΡΠΏΠ΅ΠΊΡΡΠΎΠΌ Ρ ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠΉ
The scale of virtual pKa values for calculating the isoelectric point of peptides and proteins with chemical and post-translational modifications (PTM) is presented. The learning set of pKa values is based on data from 25 experiments of isoelectric focusing of peptides with subsequent mass spectrometric identification (ProteomeXchange accession codes: PXD000065, PXD005410, PXD006291, PXD010006 and PXD017201). In order to enrich the resulting sets with peptides containing modifications the identification of peptides was repeated using raw mass spectrometry data of all datasets. In the final learning set have included peptides satisfying the following conditions: the peptide was found in the fraction with scoring function maximum and maximum peptide abundance; the peptide was found in more than one experiment, and differences of the pI value between experiments was less than 0.15 pH unit. Two variants of the scales were created. In the first variant, pKa values depended only on the residue position relative to the ends of the sequence (N- or C-terminal residue or inside the chain). In the second variant, the effect of neighboring residues was also taken into account. The prediction accuracy of the second variant was higher. The comparison with other methods of pI prediction was carried out. Although the scale was calculated from set containing only peptides, it would be applicable for pI prediction of proteins with and without PTM. The software for prediction of pI values using the resulting pKa scales is available at http://pIPredict3.ibmc.msk.ru.ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π° ΡΠΊΠ°Π»Π° Β«Π²ΠΈΡΡΡΠ°Π»ΡΠ½ΡΡ
Β» Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ pKa Π΄Π»Ρ ΡΠ°ΡΡΡΡΠ° ΠΈΠ·ΠΎΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΎΡΠΊΠΈ ΠΏΠ΅ΠΏΡΠΈΠ΄ΠΎΠ² ΠΈ Π±Π΅Π»ΠΊΠΎΠ², ΠΈΠΌΠ΅ΡΡΠΈΡ
ΠΊΠ°ΠΊ Ρ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅, ΡΠ°ΠΊ ΠΈ ΠΏΠΎΡΡΡΡΠ°Π½ΡΠ»ΡΡΠΈΠΎΠ½Π½ΡΠ΅ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ (PTM). ΠΠ±ΡΡΠ°ΡΡΠ°Ρ Π²ΡΠ±ΠΎΡΠΊΠ° Π΄Π»Ρ ΠΏΠΎΠ΄Π±ΠΎΡΠ° Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ pKa ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄Π°Π½Π½ΡΡ
ΠΈΠ· 25 ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠ² ΠΏΠΎ ΠΈΠ·ΠΎΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΎΠΌΡ ΡΠΎΠΊΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠ΅ΠΏΡΠΈΠ΄ΠΎΠ² Ρ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠ΅ΠΉ ΠΌΠ°ΡΡ-ΡΠΏΠ΅ΠΊΡΡΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠ΅ΠΉ (ProteomeXchange accession codes: PXD000065, PXD005410, PXD006291, PXD010006 ΠΈ PXD017201). ΠΠ»Ρ Π²ΡΠ΅Ρ
Π½Π°Π±ΠΎΡΠΎΠ² Π΄Π°Π½Π½ΡΡ
ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΠΏΠ΅ΠΏΡΠΈΠ΄ΠΎΠ² ΠΏΠΎ Β«ΡΡΡΡΠΌΒ» ΠΌΠ°ΡΡ-ΡΠΏΠ΅ΠΊΡΡΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Π΄Π°Π½Π½ΡΠΌ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π° Π·Π°Π½ΠΎΠ²ΠΎ Ρ ΡΠ΅Π»ΡΡ ΠΎΠ±ΠΎΠ³Π°ΡΠ΅Π½ΠΈΡ Π²ΡΠ±ΠΎΡΠΊΠΈ ΠΏΠ΅ΠΏΡΠΈΠ΄Π°ΠΌΠΈ Ρ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΡΠΌΠΈ. Π ΠΎΠΊΠΎΠ½ΡΠ°ΡΠ΅Π»ΡΠ½ΡΡ ΠΎΠ±ΡΡΠ°ΡΡΡΡ Π²ΡΠ±ΠΎΡΠΊΡ Π²ΠΊΠ»ΡΡΠ΅Π½Ρ ΠΏΠ΅ΠΏΡΠΈΠ΄Ρ, Π΄Π»Ρ ΠΊΠΎΡΠΎΡΡΡ
Π²ΡΠΏΠΎΠ»Π½ΡΠ»ΠΈΡΡ ΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅ ΡΡΠ»ΠΎΠ²ΠΈΡ: ΠΏΠ΅ΠΏΡΠΈΠ΄ Π²ΡΡΡΠ΅ΡΠ°Π»ΡΡ Π²ΠΎ ΡΡΠ°ΠΊΡΠΈΠΈ, Π΄Π»Ρ ΠΊΠΎΡΠΎΡΠΎΠΉ Π²Π΅Π»ΠΈΡΠΈΠ½Π° ΠΌΠ°ΠΊΡΠΈΠΌΡΠΌΠ° ΠΎΡΠ΅Π½ΠΎΡΠ½ΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΠΏΡΠΈ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΠ΅ΠΏΡΠΈΠ΄Π° ΡΠΎΠ²ΠΏΠ°Π΄Π°Π»Π° Ρ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΌ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ΠΌ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΠΎΡΡΠΈ (Β«abundanceΒ»), ΠΏΠ΅ΠΏΡΠΈΠ΄ Π²ΡΡΡΠ΅ΡΠ°Π»ΡΡ Π±ΠΎΠ»Π΅Π΅ ΡΠ΅ΠΌ Π² ΠΎΠ΄Π½ΠΎΠΌ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ΅, ΠΏΡΠΈΡΡΠΌ Π²Π΅Π»ΠΈΡΠΈΠ½Π° pI ΠΌΠ΅ΠΆΠ΄Ρ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°ΠΌΠΈ Π½Π΅ ΠΎΡΠ»ΠΈΡΠ°Π»Π°ΡΡ Π±ΠΎΠ»ΡΡΠ΅ ΡΠ΅ΠΌ 0.15 Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ Π΅Π΄ΠΈΠ½ΠΈΡΡ pH. Π‘ΠΎΠ·Π΄Π°Π½Ρ Π΄Π²Π° Π²Π°ΡΠΈΠ°Π½ΡΠ° ΡΠΊΠ°Π». Π ΠΏΠ΅ΡΠ²ΠΎΠΌ Π²Π΅Π»ΠΈΡΠΈΠ½Π° pKa Π·Π°Π²ΠΈΡΠ΅Π»Π° ΡΠΎΠ»ΡΠΊΠΎ ΠΎΡ Π΅Π³ΠΎ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΊΠΎΠ½ΡΠΎΠ² ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ (N- ΠΈΠ»ΠΈ C-ΠΊΠΎΠ½ΡΠ΅Π²ΠΎΠΉ ΠΎΡΡΠ°ΡΠΎΠΊ, Π»ΠΈΠ±ΠΎ Π²Π½ΡΡΡΠΈ ΡΠ΅ΠΏΠΈ). ΠΠΎ Π²ΡΠΎΡΠΎΠΌ ΡΡΠΈΡΡΠ²Π°Π»ΠΈ ΡΠ°ΠΊΠΆΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΡΠΎΡΠ΅Π΄Π½ΠΈΡ
ΠΎΡΡΠ°ΡΠΊΠΎΠ². Π’ΠΎΡΠ½ΠΎΡΡΡ ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½ΠΈΡ ΠΏΠΎ Π²ΡΠΎΡΠΎΠΌΡ Π²Π°ΡΠΈΠ°Π½ΡΡ Π±ΡΠ»Π° Π²ΡΡΠ΅. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ Ρ Π΄ΡΡΠ³ΠΈΠΌΠΈ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½ΠΈΡ pI. ΠΠ΅ΡΠΌΠΎΡΡΡ Π½Π° ΡΠΎ, ΡΡΠΎ ΡΠΊΠ°Π»Π° ΡΠ°ΡΡΡΠΈΡΡΠ²Π°Π»Π°ΡΡ ΠΏΠΎ Π²ΡΠ±ΠΎΡΠΊΠ΅, ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠ΅ΠΉ ΡΠΎΠ»ΡΠΊΠΎ ΠΏΠ΅ΠΏΡΠΈΠ΄Ρ, ΠΎΠ½Π° ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΠ° ΠΈ Π΄Π»Ρ ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½ΠΈΡ pI Π±Π΅Π»ΠΊΠΎΠ² ΠΊΠ°ΠΊ Ρ Π½Π°Π»ΠΈΡΠΈΠ΅ΠΌ PTM, ΡΠ°ΠΊ ΠΈ Π±Π΅Π·. Π‘ΠΎΠ·Π΄Π°Π½ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ΅ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠ΅ Π΄Π»Ρ ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½ΠΈΡ pI Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΡΠΊΠ°Π» pKa, Π΄ΠΎΡΡΡΠΏΠ½ΠΎΠ΅ ΠΏΠΎ Π°Π΄ΡΠ΅ΡΡ http://pIPredict3.ibmc.msk.ru
An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics
For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types
An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics
For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types
Learning with the use of distance learning technologies or what digital tools should a teacher possess?
This work is devoted to the analysis of the digital tools that a modern teacher should possess to implement the educational process using distance learning technologies. Based on the conducted research, the main competencies that a teacher should have to conduct professional activities from the point of view of students were established. Digital tools are urgently needed to implement these competencies. In this paper, we will show our view on the algorithm for using digital tools in the educational process. A teacher should have a wide arsenal of digital tools: be able to use office programs, be able to search, to select, to analyze and to interpret information. To be able to create a teacherβs website, record digital audio and video content, to be able to place it for easy access for students, and, of course, be able to use the Universityβs learning management system
About the substantiation of diagnostic indices in different categories of juveniles with delinquent behavior within the authority of the psychological, medical and educational committee
The problems of developing diagnostic indices, which could differentiate categories of deviant behavior in children and adolescents in the context of psychological, medical and educational committeesβ (PMEC) activities are considered. The main goal of PMEC is timely detection of children with peculiarities in their physical and / or mental development and / or behavior deviation, their complex psychological, medical and educational examination and, on the basis of its results, development of recommendations for the corresponding assistance and organization of their education. This group of minors includes children and adolescents not only with limited health conditions, but also with different kinds of deviant behavior and in conflict with the law. In the article, the analysis of pupilsβ personal files from special closed educational institutions for minors in conflict with the law is presented. The methodical instrument for the structured assessment of a childβs social situation of development in the work of a PMEC approved in the framework of the project βDevelopment of scientific-methodical provisions for the PMEC work concerning examination and producing recommendations for pupils with deviant behavior and in conflict with the lawβ is described and used
Hair Trace Element and Electrolyte Content in Women with Natural and In Vitro Fertilization-Induced Pregnancy
The objective of the present study was to perform comparative analysis of hair trace element content in women with natural and in vitro fertilization (IVF)-induced pregnancy. Hair trace element content in 33 women with IVF-induced pregnancy and 99 age- and body mass index-matched control pregnant women (natural pregnancy) was assessed using inductively coupled plasma mass spectrometry. The results demonstrated that IVF-pregnant women are characterized by significantly lower hair levels of Cu, Fe, Si, Zn, Ca, Mg, and Ba at pΒ <Β 0.05 or lower. Comparison of the individual levels with the national reference values demonstrated higher incidence of Fe and Cu deficiency in IVF-pregnant women in comparison to that of the controls. IVF pregnancy was also associated with higher hair As levels (pΒ <Β 0.05). Multiple regression analysis revealed a significant interrelation between IVF pregnancy and hair Cu, Fe, Si, and As content. Hair Cu levels were also influenced by vitamin/mineral supplementation and the number of pregnancies, whereas hair Zn content was dependent on prepregnancy anthropometric parameters. In turn, planning of pregnancy had a significant impact on Mg levels in scalp hair. Generally, the obtained data demonstrate an elevated risk of copper, iron, zinc, calcium, and magnesium deficiency and arsenic overload in women with IVF-induced pregnancy. The obtained data indicate the necessity of regular monitoring of micronutrient status in IVF-pregnant women in order to prevent potential deleterious effects of altered mineral homeostasis. Β© 2017, Springer Science+Business Media New York