GapMind for catabolism of small carbon sources


D-glucose catabolism

Analysis of pathway glucose in 35 genomes

Genome Best path
Acidovorax sp. GW101-3H11 gtsA, gtsB, gtsC, gtsD, glk
Azospirillum brasilense Sp245 mglA, mglB, mglC, glk
Bacteroides thetaiotaomicron VPI-5482 SSS-glucose, glk
Burkholderia phytofirmans PsJN gtsA, gtsB, gtsC, gtsD, glk
Caulobacter crescentus NA1000 MFS-glucose, glk
Cupriavidus basilensis 4G11 mglA, mglB, mglC, glk
Dechlorosoma suillum PS ptsG-crr
Desulfovibrio vulgaris Hildenborough ptsG-crr
Desulfovibrio vulgaris Miyazaki F ptsG-crr
Dinoroseobacter shibae DFL-12 aglE', aglF', aglG', aglK', glk
Dyella japonica UNC79MFTsu3.2 MFS-glucose, glk
Echinicola vietnamensis KMM 6221, DSM 17526 MFS-glucose, glk
Escherichia coli BW25113 mglA, mglB, mglC, glk
Herbaspirillum seropedicae SmR1 mglA, mglB, mglC, glk
Klebsiella michiganensis M5al mglA, mglB, mglC, glk
Magnetospirillum magneticum AMB-1 SemiSWEET, glk
Marinobacter adhaerens HP15 gtsA, gtsB, gtsC, gtsD, glk
Paraburkholderia bryophila 376MFSha3.1 gtsA, gtsB, gtsC, gtsD, glk
Pedobacter sp. GW460-11-11-14-LB5 SSS-glucose, glk
Phaeobacter inhibens BS107 aglE', aglF', aglG', aglK', glk
Pseudomonas fluorescens FW300-N1B4 gtsA, gtsB, gtsC, gtsD, glk
Pseudomonas fluorescens FW300-N2C3 gtsA, gtsB, gtsC, gtsD, glk
Pseudomonas fluorescens FW300-N2E2 gtsA, gtsB, gtsC, gtsD, glk
Pseudomonas fluorescens FW300-N2E3 gtsA, gtsB, gtsC, gtsD, glk
Pseudomonas fluorescens GW456-L13 gtsA, gtsB, gtsC, gtsD, glk
Pseudomonas putida KT2440 gtsA, gtsB, gtsC, gtsD, glk
Pseudomonas simiae WCS417 gtsA, gtsB, gtsC, gtsD, glk
Pseudomonas stutzeri RCH2 gtsA, gtsB, gtsC, gtsD, glk
Shewanella amazonensis SB2B MFS-glucose, glk
Shewanella loihica PV-4 MFS-glucose, glk
Shewanella oneidensis MR-1 ptsG, crr
Shewanella sp. ANA-3 MFS-glucose, glk
Sinorhizobium meliloti 1021 aglE', aglF', aglG', aglK', glk
Sphingomonas koreensis DSMZ 15582 MFS-glucose, glk
Synechococcus elongatus PCC 7942 MFS-glucose, glk

Confidence: high confidence medium confidence low confidence
transporter – transporters and PTS systems are shaded because predicting their specificity is particularly challenging.

This GapMind analysis is from Sep 17 2021. The underlying query database was built on Sep 17 2021.



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About GapMind

Each pathway is defined by a set of rules based on individual steps or genes. Candidates for each step are identified by using ublast (a fast alternative to protein BLAST) against a database of manually-curated proteins (most of which are experimentally characterized) or by using HMMer with enzyme models (usually from TIGRFam). Ublast hits may be split across two different proteins.

A candidate for a step is "high confidence" if either:

where "other" refers to the best ublast hit to a sequence that is not annotated as performing this step (and is not "ignored").

Otherwise, a candidate is "medium confidence" if either:

Other blast hits with at least 50% coverage are "low confidence."

Steps with no high- or medium-confidence candidates may be considered "gaps." For the typical bacterium that can make all 20 amino acids, there are 1-2 gaps in amino acid biosynthesis pathways. For diverse bacteria and archaea that can utilize a carbon source, there is a complete high-confidence catabolic pathway (including a transporter) just 38% of the time, and there is a complete medium-confidence pathway 63% of the time. Gaps may be due to:

GapMind relies on the predicted proteins in the genome and does not search the six-frame translation. In most cases, you can search the six-frame translation by clicking on links to Curated BLAST for each step definition (in the per-step page).

For more information, see the paper from 2019 on GapMind for amino acid biosynthesis, the paper from 2022 on GapMind for carbon sources, or view the source code.

If you notice any errors or omissions in the step descriptions, or any questionable results, please let us know

by Morgan Price, Arkin group, Lawrence Berkeley National Laboratory